Type: | Package |
Title: | Lipid Annotation for LC-MS/MS DDA or DIA Data |
Version: | 3.0.5 |
Author: | M Isabel Alcoriza-Balaguer |
Maintainer: | M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es> |
Description: | Lipid annotation in untargeted LC-MS lipidomics based on fragmentation rules. Alcoriza-Balaguer MI, Garcia-Canaveras JC, Lopez A, Conde I, Juan O, Carretero J, Lahoz A (2019) <doi:10.1021/acs.analchem.8b03409>. |
Depends: | R (≥ 4.0), shiny, utils, parallel, doParallel, foreach |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
LazyData: | TRUE |
RoxygenNote: | 7.3.1 |
Imports: | readMzXmlData, CHNOSZ, scales, shinythemes, stats, graphics, grDevices, iterators |
Encoding: | UTF-8 |
Suggests: | knitr, rmarkdown |
VignetteBuilder: | knitr |
NeedsCompilation: | yes |
Packaged: | 2024-05-27 12:56:08 UTC; 73581298c |
Repository: | CRAN |
Date/Publication: | 2024-05-27 14:00:05 UTC |
CEs database
Description
In silico generated database for common CEs.
Usage
data("CEdb")
Format
Data frame with 30 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
LipidMS shiny app
Description
Interactive UI for LipidMS
Usage
LipidMSapp()
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
# example data files can be download from github.com/maialba3/LipidMSv2.0_exampleFiles
library(LipidMS)
LipidMSapp()
## End(Not run)
Calculate formula and mass of acylceramides
Description
Calculate formula and mass of acylceramides
Usage
MassAcylCer(acylcer)
Arguments
acylcer |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of cholesterol esthers
Description
Calculate formula and mass of cholesterol esthers
Usage
MassCE(CE)
Arguments
CE |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of CL
Description
Calculate formula and mass of CL
Usage
MassCL(CL)
Arguments
CL |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of carnitines
Description
Calculate formula and mass of carnitines
Usage
MassCarnitines(carnitine)
Arguments
carnitine |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of ceramides
Description
Calculate formula and mass of ceramides
Usage
MassCer(cer)
Arguments
cer |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of ceramides phosphate
Description
Calculate formula and mass of ceramides phosphate
Usage
MassCerP(cerP)
Arguments
cerP |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of DG
Description
Calculate formula and mass of DG
Usage
MassDG(DG)
Arguments
DG |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of fatty acids
Description
Calculate formula and mass of fatty acids
Usage
MassFA(FA)
Arguments
FA |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of FAHFA
Description
Calculate formula and mass of FAHFA
Usage
MassFAHFA(FAHFA)
Arguments
FAHFA |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of glucoceramides
Description
Calculate formula and mass of glucoceramides
Usage
MassGlcCer(glccer)
Arguments
glccer |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of hydroxi fatty acids
Description
Calculate formula and mass of hydroxi fatty acids
Usage
MassHFA(HFA)
Arguments
HFA |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of LPA
Description
Calculate formula and mass of LPA
Usage
MassLysoPA(LPA)
Arguments
LPA |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of LPAo
Description
Calculate formula and mass of LPAo
Usage
MassLysoPAo(LPAo)
Arguments
LPAo |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of LysoPC
Description
Calculate formula and mass of LysoPC
Usage
MassLysoPC(LPC)
Arguments
LPC |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of LysoPCo
Description
Calculate formula and mass of LysoPCo
Usage
MassLysoPCo(LPCo)
Arguments
LPCo |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of LysoPCp
Description
Calculate formula and mass of LysoPCp
Usage
MassLysoPCp(LPCp)
Arguments
LPCp |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of LPE
Description
Calculate formula and mass of LPE
Usage
MassLysoPE(LPE)
Arguments
LPE |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of LPEo
Description
Calculate formula and mass of LPEo
Usage
MassLysoPEo(LPEo)
Arguments
LPEo |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of LPEp
Description
Calculate formula and mass of LPEp
Usage
MassLysoPEp(LPEp)
Arguments
LPEp |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of LPG
Description
Calculate formula and mass of LPG
Usage
MassLysoPG(LPG)
Arguments
LPG |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of LPI
Description
Calculate formula and mass of LPI
Usage
MassLysoPI(LPI)
Arguments
LPI |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of LysoPS
Description
Calculate formula and mass of LysoPS
Usage
MassLysoPS(LPS)
Arguments
LPS |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of MG
Description
Calculate formula and mass of MG
Usage
MassMG(MG)
Arguments
MG |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of PA
Description
Calculate formula and mass of PA
Usage
MassPA(PA)
Arguments
PA |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of PC
Description
Calculate formula and mass of PC
Usage
MassPC(PC)
Arguments
PC |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of PCo
Description
Calculate formula and mass of PCo
Usage
MassPCo(PCo)
Arguments
PCo |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of PCp
Description
Calculate formula and mass of PCp
Usage
MassPCp(PCp)
Arguments
PCp |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of PE
Description
Calculate formula and mass of PE
Usage
MassPE(PE)
Arguments
PE |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of plasmanyl PE
Description
Calculate formula and mass of plasmanyl PE
Usage
MassPEo(PEo)
Arguments
PEo |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of plasmenyl PE
Description
Calculate formula and mass of plasmenyl PE
Usage
MassPEp(PEp)
Arguments
PEp |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of PG
Description
Calculate formula and mass of PG
Usage
MassPG(PG)
Arguments
PG |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of PI
Description
Calculate formula and mass of PI
Usage
MassPI(PI)
Arguments
PI |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of PIP
Description
Calculate formula and mass of PIP
Usage
MassPIP(PIP)
Arguments
PIP |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of PIP2
Description
Calculate formula and mass of PIP2
Usage
MassPIP2(PIP2)
Arguments
PIP2 |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of PIP3
Description
Calculate formula and mass of PIP3
Usage
MassPIP3(PIP3)
Arguments
PIP3 |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of PS
Description
Calculate formula and mass of PS
Usage
MassPS(PS)
Arguments
PS |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of sphingomyelines
Description
Calculate formula and mass of sphingomyelines
Usage
MassSM(SM)
Arguments
SM |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of sphingoid bases
Description
Calculate formula and mass of sphingoid bases
Usage
MassSph(Sph)
Arguments
Sph |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of sphingoid phosphate bases
Description
Calculate formula and mass of sphingoid phosphate bases
Usage
MassSphP(SphP)
Arguments
SphP |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Calculate formula and mass of TG
Description
Calculate formula and mass of TG
Usage
MassTG(TG)
Arguments
TG |
character value indicating total number of carbons and double bounds |
Value
vector containing formula and mass
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
AcylCeramides database
Description
In silico generated database for common acylceramides.
Usage
data("acylcerdb")
Format
Data frame with 192 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
Adducts table
Description
Table of possible adducts to be employed by LipidMS and related information.
Usage
data("adductsTable")
Format
Data frame with 18 observations and the following 4 variables.
adduct
character vector with the adducts names.
mdiff
numeric vector indicating the mass differences.
charge
numeric vector indicating the charge.
n
numeric vector. It indicates if the ion is a monomer (1), a dimer (2), etc.
Align samples from an msbatch
Description
Align samples from an msbatch to correct time drifts during acquisition queues.
Usage
alignmsbatch(
msbatch,
dmz = 5,
drt = 30,
minsamples,
minsamplesfrac = 0.75,
span = 0.4,
parallel = FALSE,
ncores,
verbose = TRUE
)
Arguments
msbatch |
msbatch obtained from the setmsbatch function. |
dmz |
mass tolerance between peak groups in ppm. |
drt |
maximum rt distance between peaks for alignment in seconds. |
minsamples |
minimum number of samples represented in each cluster used for the alignment. |
minsamplesfrac |
minimum samples fraction represented in each cluster used for the alignment. Used to calculate minsamples in case it is missing. |
span |
span parameter for loess rt deviation smoothing. |
parallel |
logical. If TRUE, parallel processing will be performed. |
ncores |
number of cores to be used in case parallel is TRUE. |
verbose |
print information messages. |
Details
First, peak partitions are created based on the enviPick algorithm to speed up the following clustering algorithm. Briefly, peaks are ordered increasingly by mz and RT and grouped based on user-defined tolerances (dmz and drt). Each peak is initialized as a partition and then, they are evaluated to decide whether or not they can be joined to the previous partition. If mz and RT of a peak matches tolerance of any of the peaks in the previous partition, it is reassigned. Then, clustering algorithm is executed to group peaks based on their RT following the next steps for each partition:
1. Each peak in the partition is initialized as a new cluster. For each cluster we will keep the minimum, maximum and mean value of the RT, which at this point have the same values. 2. Calculate a distance matrix between all clusters. This distance will be the greatest difference between minimum and maximum values of each cluster. Distances between clusters which share peaks from the same samples will be set to NA. 3. While any distance is different to NA, search the minimum distance between two clusters. 4. If distance is below the maximum distance allowed, join clusters and update minimum, maximum and mean values, else, set distance to NA and go back to point 3.
Then, clusters with a sample representation over minsamples or minsamplesfrac, will be used for alignment. To this end, an RT matrix is built containing the RT of the peaks for each sample from the selected clusters. Then, median RT is calculated for each cluster and an RT deviation matrix is obtained. Finally, time drifts for each sample are corrected using loess regression by constructing a function based on RT deviation and median.
Value
aligned msbatch
Author(s)
M Isabel Alcoriza-Balaguer <maialba@iislafe.es>
References
Partitioning algorithm has been imported from enviPick R-package: https://cran.r-project.org/web/packages/enviPick/index.html
Examples
## Not run:
msbatch <- alignmsbatch(msbatch)
## End(Not run)
Annotate isotopes
Description
Annotate isotopes based on mass differences, retention time and peak correlation if required.
Usage
annotateIsotopes(
peaklist,
rawScans,
dmz,
drt,
massdiff,
charge,
isotopeAb,
m0mass,
corThr,
checkInt,
checkCor
)
Arguments
peaklist |
extracted peaks. Data.frame with 4 columns (mz, RT, int and peakID). |
rawScans |
raw scan data. Data.frame with 5 columns (mz, RT, int, peakID and Scan). |
dmz |
mass tolerance in ppm. |
drt |
RT windows with the same units used in peaklist. |
massdiff |
mass difference. |
charge |
charge. |
isotopeAb |
isotope natural abundance. |
m0mass |
mass of the most abundant naturally occurring stable isotope. |
corThr |
peak correlation threshold. |
checkInt |
logical. If TRUE, relative intensity is checked. |
checkCor |
logical. If TRUE, peaks correlation is checked. |
Value
peaklist with 6 columns (mz, RT, int, peakID, isotope and isoGroup).
Author(s)
M Isabel Alcoriza-Balaguer <maialba@iislafe.es>
References
Isotope annotation has been adapted from CAMERA algorithm: Kuhl C, Tautenhahn R, Boettcher C, Larson TR, Neumann S (2012). “CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets.” Analytical Chemistry, 84, 283–289. http://pubs.acs.org/doi/abs/10.1021/ac202450g.
Lipid annotation for an msbatch
Description
Summarize annotation results of an msbatch into the feature table
Usage
annotatemsbatch(
msbatch,
ppm_precursor = 5,
ppm_products = 10,
rttol = 5,
coelCutoff = 0.8,
lipidClassesPos = c("MG", "LPC", "LPE", "PC", "PCo", "PCp", "PE", "PEo", "PEp", "PG",
"PI", "Sph", "SphP", "Cer", "CerP", "AcylCer", "SM", "Carnitines", "CE", "DG", "TG"),
lipidClassesNeg = c("FA", "FAHFA", "LPC", "LPE", "LPG", "LPI", "LPS", "PC", "PCo",
"PCp", "PE", "PEo", "PEp", "PG", "PI", "PS", "Sph", "SphP", "Cer", "CerP", "AcylCer",
"SM", "CL", "BA"),
dbs,
simplifyAnnotations = FALSE,
parallel = FALSE,
ncores
)
Arguments
msbatch |
msbatch |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 5 seconds. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
lipidClassesPos |
classes of interest in ESI+. |
lipidClassesNeg |
classes of interest in ESI-. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
simplifyAnnotations |
logical. If TRUE, only the most frequent id will be kept (recommended when only pool samples have been acquired in DIA or DDA). If FALSE, all annotations will be shown. |
parallel |
logical. |
ncores |
number of cores to be used in case parallel is TRUE. |
Value
msbatch
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msbatch <- annotatemsbatch(msbatch)
msbatch$features
## End(Not run)
Load LipidMS default data bases
Description
load all LipidMS default data bases required to run identification functions.
Usage
assignDB()
Value
list of data frames
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
dbs <- assignDB()
## End(Not run)
Bile acids conjugates database
Description
Common bile acids conjugates. It can be modified to look for other BA species.
Usage
data("baconjdb")
Format
Data frame with 2 observations and the following 2 variables.
total
character vector indicating the names of the conjugates.
Mass
numeric vector with the neutral masses of the conjugates fragments.
Bile acids database
Description
In silico generated database for common bile acids.
Usage
data("badb")
Format
Data frame with 9 observations and the following 5 variables.
formula
character vector with the molecular formulas.
total
character vector containing the names of the BAs (i.e. CA, TDCA, GLCA...).
Mass
numeric vector with the neutral masses.
conjugate
character vector containing the conjugate of each BA.
base
character vector containing the core of each BA.
Process several mzXML files (peakpicking and isotope annotation) and create an msbatch for batch processing.
Description
Process several mzXML files (peakpicking and isotope annotation) and create an msbatch for batch processing.
Usage
batchdataProcessing(
files,
metadata,
polarity,
dmzagglom = 15,
drtagglom = 500,
drtclust = 100,
minpeak = c(5, 3),
drtgap = 10,
drtminpeak = 15,
drtmaxpeak = c(100, 200),
recurs = 5,
sb = c(3, 2),
sn = 2,
minint = c(1000, 100),
weight = c(2, 3),
dmzIso = 10,
drtIso = 5,
parallel = FALSE,
ncores,
verbose = TRUE
)
Arguments
files |
file paths of the mzXML files. Optional. |
metadata |
csv file or data.frame with 3 columns: sample (samples named as the mzXML files), acquisitionmode (MS, DIA or DDA) and groups (i.e. blank, QC, sample). DIA, DDA and MS files are allowed, but only DIA and DDA files will be used for lipid annotation. |
polarity |
character value: negative or positive. |
dmzagglom |
mz tolerance (in ppm) used for partitioning and clustering. |
drtagglom |
rt window used for partitioning (in seconds). |
drtclust |
rt window used for clustering (in seconds). |
minpeak |
minimum number of measurements required for a peak. |
drtgap |
maximum RT gap length to be filled (in seconds). |
drtminpeak |
minimum RT width of a peak (in seconds). At least minpeak within the drtminpeak window are required to define a peak. |
drtmaxpeak |
maximum RT width of a single peak (in seconds). |
recurs |
maximum number of peaks within one EIC. |
sb |
signal-to-base ratio. |
sn |
signal-to-noise ratio. |
minint |
minimum intensity of a peak. |
weight |
weight for assigning measurements to a peak. |
dmzIso |
mass tolerance for isotope matching. |
drtIso |
time windows for isotope matching. |
parallel |
logical. |
ncores |
number of cores to be used in case parallel is TRUE. |
verbose |
print information messages. |
Details
This function executes 2 steps: 1) creates an msobject for each sample (using the dataProcessing function) and 2) sets an msbatch (setmsbatch function).
Numeric arguments accept one or two values for MS1 and MS2, respectively.
Value
msbatch
Author(s)
M Isabel Alcoriza-Balaguer <maialba@iislafe.es>
References
Peak-picking algorithm has been imported from enviPick R-package: https://cran.r-project.org/web/packages/enviPick/index.html
See Also
Examples
## Not run:
# if metadata is a data frame:
msbatch <- batchdataProcessing(metadata$sample, metadata, polarity = "positive",
dmzagglom = 25, drtagglom = 500, drtclust = 60, minpeak = c(5, 3),
drtgap = 5, drtminpeak = 20, drtmaxpeak = 100, recurs = 5, sb = c(3, 2),
sn = 2, minint = c(1000, 100), weight = 2, dmzIso = 10, drtIso = 5)
# if metadata is a csv file:
msbatch <- batchdataProcessing(metadata = "metadata.csv", polarity = "positive",
dmzagglom = 25, drtagglom = 500, drtclust = 60, minpeak = c(5, 3),
drtgap = 5, drtminpeak = 20, drtmaxpeak = 100, recurs = 5, sb = c(3, 2),
sn = 2, minint = c(1000, 100), weight = 2, dmzIso = 10, drtIso = 5)
## End(Not run)
Carnitines database
Description
In silico generated database for common carnitines.
Usage
data("carnitinesdb")
Format
Data frame with 30 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
Total number of carbons and double bounds
Description
This function matches mz values with neutral masses from a dataframe which links masses and structures (carbons and double bounds) and extracts the structural information. It is used by identification functions to look for the structure of the previously chosen candidates.
Usage
cbs(mz, ppm, db, charge = 0)
Arguments
mz |
mz value to be matched |
ppm |
mass error tolerance |
db |
database |
charge |
numeric value indicating the charge of the ion |
Value
Character value or vector indicating structural information (carbons:bounds)
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Ceramides Phosphate database
Description
In silico generated database for common ceramides phosphate.
Usage
data("cerPdb")
Format
Data frame with 52 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
Ceramides database
Description
In silico generated database for common ceramides.
Usage
data("cerdb")
Format
Data frame with 52 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
Search of chain specific fragments
Description
Search of specific fragments that inform about the chains structure.
Usage
chainFrags(coelfrags, chainfrags, ppm = 10, candidates, f = NULL, dbs)
Arguments
coelfrags |
coeluting fragments for each candidate. Output of coelutingFrags. |
chainfrags |
character vector containing the fragmentation rules for the chain fragments. If it is an empty vector, chains will be calculated based on the difference between the precursor and the other chain. See details. |
ppm |
m/z tolerance in ppm. |
candidates |
candidates data frame. If any chain needs to be calculated based on the difference between the precursor and the other chain, this argument will be required. Output of chainFrags. |
f |
known chains. If any chain needs to be calculated based on the difference between the precursor and the other chain, this argument will be required. Output of chainFrags. |
dbs |
list of data bases required for the annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be changed. If data bases have been customized using createLipidDB, they also have to be modified here. |
Details
The chainfrags argument must contain the fragmentation rules which inform about the chains structure. For example, in the case of PG subclass, the chain in sn1 position is identified by the lysoPG as M-H resulting from the loss of the FA chain of sn2; and the chain in sn2 position is identified as the free FA chain as M-H. These two fragments need to be searched in two different steps: in the fist step we will look for lysoPGs coeluting with the precursor using chainfrags = c("lysopg_M-H"); then, we will look for FA chains using chainfrags = c("fa_M-H"). This information can be combined later using combineChains function.
To indicate the fragments to be searched, the class of lipid is writen using the same names as the LipidMS databases without the "db" at the end (i.e. pa, dg, lysopa, mg, CE, etc.), and the adduct has to be indicated as it appears in the adductsTable, both parts separated by "_". In case some chain needs to be searched based on a neutral loss, this can be defined using "NL-" prefix, followed by the database and adduct. If this neutral loss is employed to find the remaining chain, "cbdiff-" prefix allows to calculate the difference in carbons and doubles bounds between the precursor and the building block found. For example, "cbdiff-dg_M+H-H2O" will look for DG as M+H-H2O and then, it will return the difference between their number of carbons and double bounds and the ones from the precursor. Otherwise, "NL-mg_M+H-H2O" will look for fragments coming from the loss of MGs.
In case these fragments identified as losses from the precursors are going to be employed for the intensity rules, this same prefix has to be added.
If a chain is calculated based on the difference of total number of
carbons and double bounds between the precursor and a previously searched chain,
chainfrags
argument must be a character vector c("") and candidates
data frame and chain fragments list must be provided.
Value
List of data frames with the chain fragments found.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
extract chains composition from a lipid name
Description
extract chains composition from a lipid name
Usage
chains(id)
Arguments
id |
lipid name |
Value
vector with lipid class, FA position known (TRUE) or unknown (FALSE) and FA chains.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Search of class fragments to confirm the lipid class.
Description
Search of characteristic fragments that confirm a given lipid class.
Usage
checkClass(candidates, coelfrags, clfrags, ftype, clrequisites, ppm = 10, dbs)
Arguments
candidates |
output of findCandidates function. |
coelfrags |
list of peaks coeluting with each candidate. Output of coelutingFrags. |
clfrags |
vector containing the expected fragments for a given lipid class. See details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See details. |
clrequisites |
logical vector indicating if each class fragment is required or not. If none of the fragment is required, at least one of them must be present within the coeluting fragments. If the presence of any fragment excludes the class, it can be specified by using "excluding". |
ppm |
m/z tolerance in ppm. |
dbs |
list of data bases required for the annotation. By default, dbs
contains the required data frames based on the default fragmentation rules.
If these rules are modified, dbs may need to be changed. If data bases have
been customized using createLipidDB, they also have to be modified
here. It is employed when some fragment belongs to "BB" |
Details
clfrags
, ftype
and clrequisites
will indicate
the rules to confirm a lipid class. All three arguments must have the same
length.
This function allows three different types of fragments: fragments with a specific m/z as for example 227.0326 for PG in negative mode, which needs to be defined as clfrags = c(227.0326) and ftype = c("F"); neutral losses such as the head group of some PL (i.e. NL of 74.0359 in PG in negative mode), which will be defined as clfrags = c(74.0359) and ftype = c("NL"); or building blocks resulting from the loss of some groups, as for example, PA as M-H resulting from the loss of the head group (glycerol) in PG in ESI-, which will be defined as clfrags = c("pa_M-H") and ftype = c("BB"). The last two options could define the same fragments. In this case just one of them would be necessary.
When using the third type of fragment ("BB"), the building block will be specified in lower case (i.e. pa, dg, lysopa, mg, etc.) and the adduct will be given as it appears in the adductsTable, both separated by "_". Names for the building blocks are the ones used for the LipidMS databases without the "db" at the end.
In case the presence of a fragment indicates that the candidate does not
belong to the lipid class (i.e. loss of CH3 in PE, which corresponds to a PC
actually), this will be specified by using clrequisites
= c("excluding").
Value
List with 2 elements: a matrix with logical values (presence/absense) of each expected fragment (columns) for each candidate (rows), and a logical vector with the confirmation of the lipid class for each candidate.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Check intensity rules
Description
Check intensity rules to confirm chains structure.
Usage
checkIntRules(intrules, rates, intrequired, nchains, combinations, sn)
Arguments
intrules |
character vector specifying the fragments to compare. See details. |
rates |
character vector with the expected rates given as a string (i.e. "3/1"). See details. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
nchains |
number of chains of the targeted lipid class. |
combinations |
output of combineChains |
sn |
list of chain fragments identified. Object fragments of the combineChains output. |
Details
This function will be employed when the targeted lipid class has more than one chain.
Taking PG subclass as an example, intensities of lysoPG fragments (informative for sn1) can be employed to confirm the chains structure (intrules = c("lysopg_sn1/lysopg_sn1")). In this case, the intensity of lysoPG resulting from the loss of the FA chain in sn2 is at least 3 times higher (rates = c("3/1")) than the lysoPG resulting from the loss of the FA chain in sn1.
For the intrules argument, "/" will be use to separate the fragments to compare, and "_" will be use to indicate in which list of fragments we need to look for their intensities. This will depend on the chain fragments rules defined previiously.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Check intensity rules
Description
Check intensity rules to confirm chains position.
Usage
checkIntensityRules(intrules, rates, intrequired, nchains, combinations)
Arguments
intrules |
character vector specifying the fragments to compare. See details. |
rates |
character vector with the expected rates between fragments given as a string (i.e. "3/1"). See details. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
nchains |
number of chains of the targeted lipid class. |
combinations |
output of combineChains. |
Details
This function will be employed when the targeted lipid class has more than one chain.
Taking PG subclass as an example, intensities of lysoPG fragments (informative for sn1) can be employed to confirm the chains structure (intrules = c("lysopg_sn1/lysopg_sn1")). In this case, the intensity of the lysoPG resulting from the loss of the FA chain in sn2 is at least 3 times greater (rates = c("3/1")) than the lysoPG resulting from the loss of the FA chain in sn1.
For the intrules argument, "/" will be use to separate the fragments related to each chain (sn1/sn2/etc), and "_" will be use to indicate the list in which they'll be searched. This will depend on the chain fragments rules defined previously. Following the example, as we use lysoPG to define the sn1 position, both fragments will be searched in this list (sn1).
For classes with more than one FA chain, if some intensity rule should be employed to identify their position but they are no defined yet, use "Unknown". If it is not necessary because the fragmentation rules are informative enough to define the position (i.e. sphingolipid species), just leave an empty vector.
Value
List of logical vectors with the confirmation for each combination.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Cardiolipins database
Description
In silico generated database for commo CLs.
Usage
data("cldb")
Format
Data frame with 714 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
Clustering for MS peaks based on mz or RT.
Description
Clustering for MS peaks based on mz or RT.
Usage
clust(values, mins, maxs, samples, unique.samples, maxdist, ppm)
Arguments
values |
values to clusterize (mz or RT). |
mins |
lower bound for each value. |
maxs |
higher bound for each value. |
samples |
numeric vector that indicates to which sample each value belongs. |
unique.samples |
logical. FALSE if several measures from the same sample can be clusterized together. |
maxdist |
maximum distance allowed within a cluster. |
ppm |
logical. TRUE if maxdist is given in ppm. |
Value
numeric vector with the assigned clusters
Author(s)
M Isabel Alcoriza-Balaguer <maialba@iislafe.es>
Calculate max distance between clusters.
Description
Calculate max distance between clusters.
Usage
clustdist(mins, maxs)
Arguments
mins |
lower bound for each cluster. |
maxs |
higher bound for each cluster. |
Value
numeric vector with the assigned clusters
Author(s)
M Isabel Alcoriza-Balaguer <maialba@iislafe.es>
EIC extraction based on previous partitions generated by partitioning
Description
EIC extraction based on previous partitions generated by partitioning
Usage
clustering(msobject, dmzagglom, drtclust, minpeak, mslevel, cE)
Arguments
msobject |
msobject generated by partitioning |
dmzagglom |
mz tolerance for clusters |
drtclust |
RT window for clusters |
minpeak |
minimum number of measures to define a peak |
mslevel |
info to access msobject |
cE |
info to access msobject |
Value
msobject
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
References
Peak-picking algorithm has been imported from enviPick R-package: https://cran.r-project.org/web/packages/enviPick/index.html
Coeluting fragments extraction
Description
Given a RT and a list of peaks, this function subsets all coeluting fragments within a rt windows. It is used by identification functions to extract coeluting fragments from high energy functions for candidate precursor ions.
Usage
coelutingFrags(
precursors,
products,
rttol,
rawData = data.frame(),
coelCutoff = 0
)
Arguments
precursors |
candidates data frame. Output of findCandidates. |
products |
peaklist for MS2 function (MSMS). |
rttol |
rt window in seconds. |
rawData |
raw scans data. Output of dataProcessing function (MSMS$rawData). |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. |
Value
List of data frames with the coeluting fragments for each candidate.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
calculate coelution score between two peaks
Description
Calculate coelution score between two peaks.
Usage
coelutionScore(peak1, peak2, rawData)
Arguments
peak1 |
character vector specifying the peakID of the first peak. |
peak2 |
character vector specifying the peakID of the second peak. |
rawData |
data frame with raw data for each scan. it need to have at least 5 columns: mz, RT, int, Scan (ordinal number for a given MS function) and peakID (peakID to which it has been assigned). #' @keywords internal |
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Combine chain fragments that could belong to the same precursor.
Description
It calculates combinations of chain fragments that sum up the same number of carbons and double bounds as the precursor.
Usage
combineChains(candidates, nchains, sn1, sn2, sn3, sn4)
Arguments
candidates |
candidates data frame. Output of findCandidates. |
nchains |
number of chains of the targeted lipid class. |
sn1 |
list of chain fragments identified for sn1 position. Output of chainFrags. |
sn2 |
list of chain fragments identified for sn2 position. Output of chainFrags. If required. |
sn3 |
list of chain fragments identified for sn3 position. Output of chainFrags. If required. |
sn4 |
list of chain fragments identified for sn4 position. Output of chainFrags. If required. |
Value
List of data frames with candidate chains structures.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Confidence Annotation Levels
Description
Confidence annotation levels and their hierarchy.
Usage
data("confLevels")
Format
Data frame with 5 observations and 2 variables.
level
character vector with the names of the annotation levels.
order
numeric vector that indicates the hierarchichal order.
Customizable lipid DBs creator
Description
It allows to create easy-customizable lipid DBs for annotation with LipidMS package.
Usage
createLipidDB(lipid, chains, chains2)
Arguments
lipid |
character value indicating the class of lipid. See Details. |
chains |
character vector indicating the FA chains to be employed |
chains2 |
character vector containing the sphingoid bases to be employed if required. |
Details
lipidClass
argument needs to be one of the following
character values: "Cer", "CerP", "GlcCer", "SM", "Carnitine", "CE", "FA",
"HFA", "Sph" (sphingoid bases), "SphP", "MG", "LPA", , "LPC",
"LPE", "LPG", "LPI", "LPS", "FAHFA", "DG", "PC", "PE", "PG", "PI", "PS",
"PA", "TG", "CL" or "all".
Value
List with the requested dbs (data frames)
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
fas <- c("8:0", "10:0", "12:0", "14:0", "14:1", "15:0", "16:0", "16:1",
"17:0", "18:0", "18:1", "18:2", "18:3", "18:4", "20:0", "20:1", "20:2",
"20:3", "20:4", "20:5", "22:0", "22:1", "22:2", "22:3", "22:4", "22:5",
"22:6", "24:0", "24:1", "26:0")
sph <- c("16:0", "16:1", "18:0", "18:1")
newdb <- createLipidDB(lipid = "PC", chains = fas, chains2 = sph)
Cross different candidates tables to remove false positives.
Description
This function crosses tables of precursor candidates identified using different adducts.
Usage
crossAdducts(df1, df2, rttol, rawData, coelCutoff)
Arguments
df1 |
data frame containing identification results using the main adduct |
df2 |
data frame containing identification results using a secondary adduct |
rttol |
retention time tolerance in seconds |
rawData |
raw scans data. Output of dataProcessing function (MS1$rawData). |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. |
Value
Subset of the original data frame without adducts
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Cross the original MS1 peaklist with the annotation results
Description
Cross the original MS1 peaklist with the annotation results.
Usage
crossTables(msobject, ppm = 5, rttol = 10, dbs)
Arguments
msobject |
annotated msobject |
ppm |
mass tolerance in ppm. |
rttol |
rt tolerance to match peaks in seconds. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
Value
msobject with an annotatedPeaklist, which is a data frame with 6 columns: mz, RT, int, LipidMSid, adduct and confidence level for the annotation. When multiple IDs are proposed for the same feature, they are sorted based on the annotation level and score.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Process mzXML files individually: peakpicking and isotope annotation
Description
Process mzXML files individually: peakpicking and isotope anotation
Usage
dataProcessing(
file,
acquisitionmode,
polarity,
dmzagglom = 15,
drtagglom = 500,
drtclust = 100,
minpeak = c(5, 3),
drtgap = 10,
drtminpeak = c(15, 15),
drtmaxpeak = c(100, 200),
recurs = 5,
sb = c(3, 2),
sn = 2,
minint = c(1000, 100),
weight = c(2, 3),
dmzIso = 5,
drtIso = 5,
verbose = TRUE
)
Arguments
file |
file path. |
acquisitionmode |
character value: MS, DIA or DDA. |
polarity |
character value: negative or positive. |
dmzagglom |
mz tolerance (in ppm) used for partitioning and clustering. |
drtagglom |
RT window used for partitioning (in seconds). |
drtclust |
RT window used for clustering (in seconds). |
minpeak |
minimum number of measurements required for a peak. |
drtgap |
maximum RT gap length to be filled (in seconds). |
drtminpeak |
minimum RT width of a peak (in seconds). At least minpeak within the drtminpeak window are required to define a peak. |
drtmaxpeak |
maximum RT width of a single peak (in seconds). |
recurs |
maximum number of peaks within one EIC. |
sb |
signal-to-base ratio. |
sn |
signal-to-noise ratio. |
minint |
minimum intensity of a peak. |
weight |
weight for assigning measurements to a peak. |
dmzIso |
mass tolerance for isotope matching. |
drtIso |
time window for isotope matching. |
verbose |
print information messages. |
Details
It is important that mzXML files are centroided.
This function executes 2 steps: 1) peak-picking based on enviPick package and 2) isotope annotation based on CAMERA algorithm.
Numeric arguments accept one or two values for MS1 and MS2, respectively.
Value
an msobject that contains metadata of the mzXML file, raw data and extracted peaks.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@iislafe.es>
References
Peak-picking algorithm has been imported from enviPick R-package: https://cran.r-project.org/web/packages/enviPick/index.html
Isotope annotation has been adapted from CAMERA algorithm: Kuhl C, Tautenhahn R, Boettcher C, Larson TR, Neumann S (2012). “CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets.” Analytical Chemistry, 84, 283–289. http://pubs.acs.org/doi/abs/10.1021/ac202450g.
See Also
batchdataProcessing and setmsbatch
Examples
## Not run:
msobject <- dataProcessing("input_file.mzXML", acquisitionmode="DIA", polarity,
dmzagglom = 25, drtagglom = 500, drtclust = 60, minpeak = c(5, 3),
drtgap = 5, drtminpeak = 20, drtmaxpeak = 100, recurs = 5, sb = c(3, 2),
sn = 2, minint = c(1000, 100), weight = 2, dmzIso = 10, drtIso = 5)
## End(Not run)
Creation of a database for C.
Description
Creation of a database CL.
Usage
dbFourChains(chains, lipid)
Arguments
chains |
character vector indicating the FAs to be employed |
lipid |
character value indication the class of lipid. |
Value
data frame containing formula, mass and total number of carbons and insaturations
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Creation of a database for Carnitines, CE, FA, HFA, LPL, MG, sphingoid bases and sphingoid bases phosphate.
Description
Creation of a database for Carnitines, CE, FA, HFA, LPL, MG, sphingoid bases and sphingoid bases phosphate.
Usage
dbOneChain(chains, lipid)
Arguments
chains |
character vector indicating the FAs to be employed |
lipid |
character value indication the class of lipid. |
Value
data frame containing formula, mass and total number of carbons and insaturations
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Creation of a database for Cer, CerP, GlcCer and SM
Description
Creation of a database for Cer, CerP, GlcCer and SM
Usage
dbSphingolipids(chains, chains2, lipid)
Arguments
chains |
character vector indicating the FAs to be employed |
chains2 |
character vector indicating the sphingoid bases to be employed |
lipid |
character value indication the class of lipid. |
Value
data frame containing formula, mass and total number of carbons and insaturations.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Creation of a database for TG.
Description
Creation of a database for TG.
Usage
dbThreeChains(chains, lipid)
Arguments
chains |
character vector indicating the FAs to be employed |
lipid |
character value indication the class of lipid. |
Value
data frame containing formula, mass and total number of carbons and insaturations
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Creation of a database for FAHFA, DG and PL.
Description
Creation of a database for FAHFA, DG and PL.
Usage
dbTwoChains(chains, lipid)
Arguments
chains |
character vector indicating the FAs to be employed |
lipid |
character value indication the class of lipid. |
Value
data frame containing formula, mass and total number of carbons and insaturations
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
MS/MS scan extraction of a precursor in DDA
Description
This function searches for the closest precursor selected for MS2 in DDA that matches mz tolerance and RT window of a list of candidates and extracts their fragments.
Usage
ddaFrags(candidates, precursors, rawData, ppm)
Arguments
candidates |
candidates data frame. Output of findCandidates. |
precursors |
data frame with the whole list of precursors selected for MS2. |
rawData |
peaklist for MS2 function (MSMS). |
ppm |
m/z tolerance in ppm. |
Details
MS2 scans for a given precursor are searched within a rt window from minrt-rttol/2 to maxrt+rttol/2. If the same precursor was selected several times along the peak, the closest scan to the rt at the peak maximum is selected for annotation.
Coelution score for DDA fragments represents their relative intensity within the MS2 scan.
Value
List of data frames with the fragments for each candidate.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
DGs database
Description
In silico generated database for common DGs.
Usage
data("dgdb")
Format
Data frame with 147 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
Difference between two carbon:bounds structures
Description
This function calculates the number of carbon and double bounds that differ between two structures.
Usage
diffcb(total, frag, db)
Arguments
total |
character value indicating the precursor structure |
frag |
character value indicating the fragment structure |
db |
db of chains to be considered |
Value
Character value
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
FAs database
Description
In silico generated database for common FAs.
Usage
data("fadb")
Format
Data frame with 30 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
FAHFAs database
Description
In silico generated database for common FAHFAs.
Usage
data("fahfadb")
Format
Data frame with 147 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
Fill peaks from a grouped msbatch
Description
Use grouping results to target all peaks from the msbatch in each sample and refill intensities at the features table.
Usage
fillpeaksmsbatch(msbatch)
Arguments
msbatch |
msbatch obtained from the groupmsbatch function. |
Details
Once grouping has been performed, areas are extracted again for each peak and sample based on the peak parameters defined for each feature (mz and tolerance and initial and end RT).
Value
msbatch
Author(s)
M Isabel Alcoriza-Balaguer <maialba@iislafe.es>
Examples
## Not run:
msbatch <- fillpeaksmsbatch(msbatch)
## End(Not run)
Presence or absence of an mz value within a vector of mz values
Description
This function indicates the presence or absence of a fragment within a set of mz values with certain tolerance. It is used by identification functions to look for the generic fragments of each class of lipid.
Usage
filtermsms(fragments, frag, ppm)
Arguments
fragments |
vector of mz values |
frag |
mz to be matched |
ppm |
mass tolerance |
Value
Logical value
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Remove low adduct supported candidates to avoid false positives.
Description
In case some feature has been annotated to different candidate species, this function removes the one with less adducts assigned.
Usage
filtrateAdducts(df)
Arguments
df |
data frame containing candidates |
Value
Subset of the original data frame
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Search of lipid candidates of a certain class
Description
Search of lipid candidates from a peaklist based on a set of expected adducts.
Usage
findCandidates(
MS1,
db,
ppm,
rt,
adducts,
rttol = 3,
dbs,
rawData = data.frame(),
coelCutoff = 0
)
Arguments
MS1 |
peaklist of the MS function. Data frame with 3 columns: mz, RT (in seconds) and int (intensity). |
db |
database (i.e. pcdb, dgdb, etc.). Data frame with at least 2 columns: Mass (exact mass) and total (total number of carbons and double bound of the FA chains, i.e. "34:1"). |
ppm |
m/z tolerance in ppm. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
character vector containing the expected adducts to search for (i.e. "M+H", "M+Na", "M-H", etc.). See details. |
rttol |
rt tolerance in seconds to match adducts. |
dbs |
list of data bases required for the annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be changed. If data bases have been customized using createLipidDB, they also have to be modified here. |
rawData |
raw scans data. Output of dataProcessing function (MS1$rawData). |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. |
Details
findCandidates looks for matches between the m/z of the MS1 peaklist and the expected m/z of the candidates in the database for each adduct. If several adducts are expected, results are combined.
Adducts allowed are contained in adductsTable data frame, which can be modified if required (see adductsTable).
Value
Data frame with the found candidates. It contains 6 columns: mz, RT, int (from the peaklist data.frame), ppms, cb (total number of carbons and double bounds of the FA chains) and adducts.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
find lisnks between MS1 peaks and precursors selected for MS2 in DDA
Description
Find lisnks between MS1 peaks and precursors selected for MS2 in DDA.
Usage
findMS2precursor(mz, minrt, maxrt, precursors, ppm)
Arguments
mz |
mz |
minrt |
minimum intensity |
maxrt |
maximum intensity |
precursors |
data frame with all precursors |
ppm |
mass tolerance |
Value
peak-pick based on previous EIC clusters generated by clustering
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Find candidate precursor from fullMS function
Description
This function is employed by all identification function in this package to find possible precursors in the fullMS function.
Usage
findPrecursor(MS1, db, ppm, massdif, rt, n = 1, charge = 1)
Arguments
MS1 |
data frame containing m/z, RT and intensity of ions from the first function |
db |
database to be searched for matches |
massdif |
mass difference between neutral mass and the adduct expected |
rt |
rt window |
n |
numeric value indicating whether to look for monomers (1), dimers (2), etc. |
charge |
numeric value indicating the expected charge of the ions |
Value
Subset of the original data frame without adducts
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Search for fragments of interest withing a list of coeluting fragments
Description
Given a set of coeluting fragments, this function searches for matches within a database. It is used by identification functions to extract fragments of interest based on the fragmentation patterns of each class of lipid.
Usage
frags(df, ppm, db, mdiff, charge, n)
Arguments
df |
data frame containing coeluting fragments |
ppm |
mass tolerance |
db |
database (data frame with two columns) where to look into |
charge |
mdiff |
Value
Data frame containing matched ions information
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Get formula and neutral mass for annotated compounds
Description
Get formula and neutral mass for annotated compounds.
Usage
getFormula(df, dbs)
Arguments
df |
data frame with the input results |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
Value
Data frame
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Obtain an inclusion list from the annotation results
Description
Obtain an inclusion list for the identified lipids.
Usage
getInclusionList(df, dbs)
Arguments
df |
data frame. Output of identification functions (results table from an msobject or feature table from an msbatch). |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
Value
Data frame with 6 columns: formula, RT, neutral mass, m/z, adduct and the LipidMSid.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Extract peaks from all msobjects in a msbatch.
Description
Extract peaks from all MS1 peaklists of the msobjects in a msbatch.
Usage
getallpeaks(msbatch)
Arguments
msbatch |
msbatch. |
Value
data.frame with 6 columns (mz, RT, int, peakID, isotope and isoGroup).
Author(s)
M Isabel Alcoriza-Balaguer <maialba@iislafe.es>
Write features table based on groups
Description
Write features table based on groups
Usage
getfeaturestable(msbatch)
Arguments
msbatch |
Value
data.frame
Author(s)
M Isabel Alcoriza-Balaguer <maialba@iislafe.es>
Group features from an msbatch
Description
Group features from an msbatch
Usage
groupmsbatch(
msbatch,
dmz = 5,
drtagglom = 30,
drt = 15,
minsamples,
minsamplesfrac = 0.25,
parallel = FALSE,
ncores,
verbose = TRUE
)
Arguments
msbatch |
msbatch obtained from setmsbatch or alignmsbatch functions. |
dmz |
mass tolerance between peak groups for grouping in ppm. |
drtagglom |
rt window for mz partitioning. |
drt |
rt window for peaks clustering. |
minsamples |
minimum number of samples represented in clusters used for grouping. |
minsamplesfrac |
minimum samples fraction represented in each cluster used for grouping. Used to calculate minsamples in case it is missing. |
parallel |
logical. If TRUE, parallel processing is performed. |
ncores |
number of cores to be used in case parallel is TRUE. |
verbose |
print information messages. |
Details
First, peak partitions are created based on the enviPick algorithm to speed up the following clustering algorithm. Briefly, peaks are ordered increasingly by mz and RT and grouped based on user-defined tolerances (dmz and drt). Each peak is initialized as a partition and then, they are evaluated to decide whether or not they can be joined to the previous partition. If mz and RT of a peak matches tolerance of any of the peaks in the previous partition, it is reassigned. Then, clustering algorithm is executed to improve these partitions based on their mz following the next steps for each partition:
1. Each peak in the partition is initialized as a new cluster. For each cluster we will keep the minimum, maximum and mean value of the mz, which at this point have the same values. 2. Calculate a distance matrix between all clusters. This distance will be the greatest difference between minimum and maximum values of each cluster. 3. While any distance is different to NA, search the minimum distance between two clusters. 4. If distance is below the maximum distance allowed, join clusters and update minimum, maximum and mean values, else, set distance to NA and go back to point 3.
Then this same clustring algorithm is executed again to group peaks based on their RT. In this case, distances between clusters which share peaks from the same samples will be set to NA.
After groups have been defined, those clusters with a sample representation over minsamples or minsamplesfrac will be used for building the feature table.
Value
grouped msbatch
Author(s)
M Isabel Alcoriza-Balaguer <maialba@iislafe.es>
References
Partitioning algorithm has been imported from enviPick R-package: https://cran.r-project.org/web/packages/enviPick/index.html
Examples
## Not run:
msbatch <- groupmsbatch(msbatch)
## End(Not run)
HFAs database
Description
In silico generated database for common HFAs.
Usage
data("hfadb")
Format
Data frame with 30 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
Acylceramides (AcylCer) annotation for ESI-
Description
AcylCer identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idAcylCerneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M-H", "M+CH3COO"),
clfrags = c(),
clrequired = c(),
ftype = c(),
chainfrags_sn1 = c("cbdiff-cer_M-H"),
chainfrags_sn2 = c("sph_Mn-62.06001", "sph_M-H-H2O"),
chainfrags_sn3 = c("fa_Mn-1.9918", "fa_Mn-19.0179"),
intrules = c("cbdiff-cer_sn1/sph_sn2", "sph_sn2/fa_sn3"),
rates = c("5/1", "2/1"),
intrequired = c(T, T),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for AcylCer in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the sphingoid base. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
chainfrags_sn3 |
character vector containing the fragmentation rules for the acyl chain. See chainFrags for details. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected ratesbetween fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idAcylCerneg
function involves 5 steps. 1) FullMS-based
identification of candidate AcylCer as M-H and M+CH3COO. 2) Search of AcylCer
class fragments: no class fragments by default. 3) Search of specific fragments
that inform about the acyl chain (Cer as M-H), the sphingoid base (neutral
loss of 62.0600 of the Sph) and the FA chain (FA as M-H and M-H2O but with a N
instead of an O, what results in a mass differences of 1.9918 and 19.0179
respectively). 4) Look for possible chains structure based on the combination
of chain fragments. 5) Check intensity rules to confirm chains position. In
this case, the fragment coming from the loss of the acyl chain must be at least
5 times more intense the fragment from the sphingoid base and this one, two
times more intense than the FA chain from sn3.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idAcylCerneg(msobject)
## End(Not run)
Acylceramides (AcylCer) annotation for ESI+
Description
AcylCer identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idAcylCerpos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H", "M+H-H2O", "M+Na"),
clfrags = c(),
clrequired = c(),
ftype = c(),
chainfrags_sn1 = c("cbdiff-cer_M+H", "cbdiff-cer_M+H-H2O", "cbdiff-cer_M+H-2H2O"),
chainfrags_sn2 = c("sph_M+H-H2O", "sph_M+H-2H2O"),
chainfrags_sn3 = c("fa_Mn+0.02329"),
intrules = c("sph_sn2/cbdiff-cer_sn1", "sph_sn2/fa_sn3"),
rates = c("2/1", "5/1"),
intrequired = c(T, T),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for Cer in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the sphingoid base. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
chainfrags_sn3 |
character vector containing the fragmentation rules for the acyl chain. See chainFrags for details. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected ratesbetween fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idAcylCerpos
function involves 5 steps. 1) FullMS-based
identification of candidate AcylCer as M+H, M+H-H2O and M+Na. 2) Search of
AcylCer class fragments: there are no class fragments by default. 3) Search of
specific fragments that inform about the acyl chain (Cer as M+H, M+H-H2O or
M+H-2H2H), the sphingoid base (Sph as M+H-H2O or M+H-2H2O) and the FA chain
(FA as M+H but with a N intead of an O, what results in a mass difference of
0.02329 with the Mn of the FA chain). 4) Look for possible chains structure
based on the combination of chain fragments. 5) Check intensity rules to
confirm chains position. In this case, Sph fragment must be twice more
intense than the loss of the acyl chain and at least 5 times more intense than
the FA chain from sn3.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idCerPneg(msobject)
## End(Not run)
Bile Acids (BA) annotation for ESI-
Description
BA identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idBAneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M-H"),
conjfrag = c("baconj_M-H"),
bafrag = c("ba_M-H-H2O", "ba_M-H-2H2O"),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for BA in ESI-. Adducts allowed can be modified in the adducsTable (dbs argument). |
conjfrag |
character vector containing the fragmentation rules for the BA-conjugates. By default just taurine and glycine are considered, but baconjdb can be modified to add more possible conjugates. See chainFrags for details. It can also be an empty vector. |
bafrag |
character vector containing the fragmentation rules for other BA fragments. See chainFrags for details. It can be an empty vector. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idBAneg
function involves 3 steps. 1) FullMS-based
identification of candidate BA as M-H. 2) Search of BA-conjugate fragments if
required. 3) Search of fragments coming from the loss of H2O.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (MS-only if no rules are defined, or Subclass level if they are supported by fragments) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idBAneg(msobject)
## End(Not run)
Cholesteryl Esters (CE) annotation for ESI+
Description
CE identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idCEpos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("2M+NH4", "2M+Na", "M+NH4", "M+Na"),
clfrags = c(369.3516, "fa_M+H-H2O"),
clrequired = c(F, F),
ftype = c("F", "BB"),
chainfrags_sn1 = c("fa_M+H-H2O"),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for CE in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments. See chainFrags for details. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idCEpos
function involves 3 steps. 1) FullMS-based
identification of candidate CE as 2M+NH4, 2M+Na, M+NH4 and M+Na. 2) Search of
CE class fragments: 369.3516 or its loss (FA as M+H-H20) coeluting with the
precursor ion. 3) Search of specific fragments that confirm chain composition
(FA as M+H-H2O).
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as CE only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idCEpos(msobject)
## End(Not run)
Cardiolipines (CL) annotation for ESI-
Description
CL identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idCLneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 5,
rt,
adducts = c("M-H", "M+Na-2H"),
clfrags = c(),
clrequired = c(),
ftype = c(),
chainfrags_sn1 = c("lysopa_M-H-H2O"),
chainfrags_sn2 = c("lysopa_M-H-H2O"),
chainfrags_sn3 = c("lysopa_M-H-H2O"),
chainfrags_sn4 = c("lysopa_M-H-H2O"),
intrules = c("Unknown"),
rates = c(),
intrequired = c(),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for CL in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. |
chainfrags_sn3 |
character vector containing the fragmentation rules for the chain fragments in sn3 position. See chainFrags for details. |
chainfrags_sn4 |
character vector containing the fragmentation rules for the chain fragments in sn4 position. See chainFrags for details. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. If some intensity rules should be employed to identify the chains position but they are't known yet, use "Unknown". If it isn't required, leave an empty vector. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idCLneg
function involves 5 steps. 1) FullMS-based
identification of candidate CL as M-H or M-2H. 2) Search of CL class fragments:
no class fragments are searched by defaults as they use to have bad coelution
scores. 3) Search of specific fragments that inform about chain composition
at sn1 (lysoPA as M-H-H2O), sn2 (lysoPA as M-H-H2O), sn3 (lysoPA as M-H-H2O)
and sn4 (lysoPA as M-H-H2O). 4) Look for possible chains structure based on
the combination of chain fragments. 5) Check intensity rules to confirm
chains position. For CL there are no intensity rules by default.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idCLneg(msobject)
## End(Not run)
Acylcarnitine annotation for ESI+
Description
Acylcarnitines identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idCarpos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H", "M+Na"),
clfrags = c(60.0807, 85.0295, "fa_M+H-H2O"),
clrequired = c(F, F, F),
ftype = c("F", "F", "BB"),
chainfrags_sn1 = c("fa_M+H-H2O"),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for Carnitines in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments. See chainFrags for details. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idCarpos
function involves 3 steps. 1) FullMS-based
identification of candidate carnitines as M+H and M+Na. 2) Search of
carnitine class fragments: 60.0807 and 85.0295 or its loss (FA as M+H-H20)
coeluting with the precursor ion. 3) Search of specific fragments coming from
the FA chain (FA as M+H-H2O).
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as Carnitines only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idCarpos(msobject)
## End(Not run)
Ceramides phosphate (CerP) annotation for ESI-
Description
CerP identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idCerPneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M-H"),
clfrags = c(78.9585, 96.9691),
clrequired = c(F, F),
ftype = c("F", "F"),
chainfrags_sn1 = c("sphP_M-H"),
chainfrags_sn2 = c("fa_Mn-1.9918", ""),
intrules = c(),
rates = c(),
intrequired = c(),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for CerP in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected ratesbetween fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idCerPneg
function involves 5 steps. 1) FullMS-based
identification of candidate CerP as M-H. 2) Search of CerP class
fragments: 78.9585 and 96.9691. 3) Search of specific fragments that inform
about the sphingoid base (SphP as M-H resulting from the loss of the FA chain)
and the FA chain (FA as M-H but with a N instead of an O, what results in a
mass difference of 1.9918 from the exact mass of the FA, or the difference
between precursor and sn1 chain fragments). 4) Look for possible chains
structure based on the combination of chain fragments. 5) Check intensity
rules to confirm chains position. In this case, there are no intensity rules
by default.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idCerPneg(msobject)
## End(Not run)
Ceramides phosphate (CerP) annotation for ESI+
Description
CerP identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idCerPpos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H"),
clfrags = c("cer_M+H-H2O", "cer_M+H-2H2O"),
clrequired = c(F, F),
ftype = c("BB", "BB"),
chainfrags_sn1 = c("sph_M+H-2H2O"),
chainfrags_sn2 = c(""),
intrules = c(),
rates = c(),
intrequired = c(),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for Cer in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between peaks (adducts, parent and fragment ions...). Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idCerPpos
function involves 5 steps. 1) FullMS-based
identification of candidate CerP as M+H. 2) Search of Cer class fragments:
Cer as M+H-H2O and M+H-2H2O resulting from the loss of the phosphate group and
1 or 2 H2O molecules. 3) Search of specific fragments that inform about the
sphingoid base (Sph as M+H-2H2O resulting from the loss of the FA chain and
the phosphate group) and the FA chain (by default it is calculated using the
difference between precursor and sph fragments). 4) Look for possible chains
structure based on the combination of chain fragments. 5) Check intensity
rules to confirm chains position. In this case, there are no intensity rules
by default.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idCerPpos(msobject)
## End(Not run)
Ceramides (Cer) annotation for ESI-
Description
Cer identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idCerneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M-H", "M+CH3COO"),
clfrags = c(),
clrequired = c(),
ftype = c(),
chainfrags_sn1 = c("NL-nlsph_M-H", "sph_M-H-2H2O", "sph_M-H-H2O"),
chainfrags_sn2 = c("fa_Mn-1.9918", "fa_M-H-H2O"),
intrules = c(),
rates = c(),
intrequired = c(),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for Cer in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected ratesbetween fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idCerneg
function involves 5 steps. 1) FullMS-based
identification of candidate Cer as M-H and M+CH3COO. 2) Search of Cer class
fragments: there are no class fragment by default. 3) Search of specific
fragments that inform about the sphingoid base (Sph as M-H-2H2O resulting
from the loss of the FA chain or loss of part of the sphingoid base) and the
FA chain (FA as M-H but with a N instead of an O, what means a mass difference
of 1.9918 from the exact mass of the FA or FA as M-H-H2O). 4) Look for
possible chains structure based on the combination of chain fragments.
5) Check intensity rules to confirm chains position. In this case, there are
no intensity rules by default.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idCerneg(msobject)
## End(Not run)
Ceramides (Cer) annotation for ESI+
Description
Ceramides identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idCerpos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H-H2O", "M+Na", "M+H"),
clfrags = c(),
clrequired = c(),
ftype = c(),
chainfrags_sn1 = c("sph_M+H-2H2O"),
chainfrags_sn2 = c(""),
intrules = c(),
rates = c(),
intrequired = c(),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for Cer in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between peaks (adducts, parent and fragment ions...). Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idCerpos
function involves 5 steps. 1) FullMS-based
identification of candidate Cer as M+H, M+H-H2O and M+Na. 2) Search of Cer
class fragments: there isn't any class fragment by default. 3) Search of
specific fragments that inform about the sphingoid base (Sph as M+H-2H2O
resulting from the loss of the FA chain) and the FA chain (by default it is
calculated using the difference between precursor and sph fragments).
4) Look for possible chains structure based on the combination of chain
fragments. 5) Check intensity rules to confirm chains position. In this case,
there are no intensity rules by default.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idCerpos(msobject)
## End(Not run)
Diacylglycerols (DG) annotation for ESI+
Description
DG identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idDGpos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H-H2O", "M+NH4", "M+Na"),
clfrags = c(),
clrequired = c(),
ftype = c(),
chainfrags_sn1 = c("mg_M+H-H2O"),
chainfrags_sn2 = c("mg_M+H-H2O"),
intrules = c("mg_sn1/mg_sn2"),
rates = c("1"),
intrequired = c(T),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for DG in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idDGpos
function involves 5 steps. 1) FullMS-based
identification of candidate DG as M+H-H2O, M+NH4 and M+Na. 2) Search of DG
class fragments: there are no class fragment by default. 3) Search of
specific fragments that inform about the FA chains (MGs as M+H-H2O
resulting from the loss of the FA chains). 4) Look for possible chains
structure based on the combination of chain fragments. 5) Check intensity
rules to confirm chains position: MG coming from the loss of the sn2 chain is
more intense than the one coming from the loss of sn1.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idDGpos(msobject)
## End(Not run)
FAHFA annotation for ESI-
Description
FAHFA identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idFAHFAneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M-H"),
clfrags = c(),
clrequired = c(),
ftype = c(),
chainfrags_sn1 = c("hfa_M-H"),
chainfrags_sn2 = c("fa_M-H"),
intrules = c("hfa_sn1/fa_sn2"),
rates = c("3/1"),
intrequired = c(T),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for FAHFA in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idFAHFAneg
function involves 5 steps. 1) FullMS-based
identification of candidate FAHFA as M-H. 2) Search of FAHFA class fragments:
there is't any class fragment by default. 3) Search of specific fragments
that inform about chain composition in sn1 (HFA as M-H resulting from the
loss of the FA chain) and sn2 (FA chain as M-H). 4) Look for possible
chains structure based on the combination of chain fragments. 5) Check
intensity rules to confirm chains position. In this case, HFA intensity has
to be higher than FA.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idFAHFAneg(msobject)
## End(Not run)
Fatty Acids (FA) annotation for ESI-
Description
FA identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idFAneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M-H", "2M-H"),
clfrags = c("fa_M-H", "fa_M-H-H2O"),
clrequired = c(FALSE, FALSE),
ftype = c("BB", "BB"),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for FA in ESI-. Adducts allowed can be modified in addutcsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idFAneg
function involves 2 steps. 1) FullMS-based
identification of candidate FA as M-H or 2M-H. 2) Search of FA class
fragments: neutral loss of H2O coeluting with the precursor ion or the
molecular ion.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, just MS-only or Subclass level (if any class fragment is defined) are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idFAneg(msobject)
## End(Not run)
Lysophosphocholines (LPC) annotation for ESI-
Description
LPC identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idLPCneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+CH3COO", "M-CH3", "M+CH3COO-CH3"),
clfrags = c(168.0426, 224.0688, "lysopa_M-H", "lysopc_M-CH3"),
clrequired = c(F, F, F, F),
ftype = c("F", "F", "BB", "BB"),
chainfrags_sn1 = c("fa_M-H"),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for LPC in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments. See chainFrags for details. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idLPCneg
function involves 3 steps. 1) FullMS-based
identification of candidate LPC as M+CH3COO, M-CH3 and M+CH3COO-CH3. To avoid
incorrect annotations of PE as PC, candidates which are present just as M-CH3
will be ignored. 2) Search of LPC class fragments: 168.0426, 224.0688, lysoPA
as M-H or lysoPC as M-CH3 coeluting with the precursor ion. 3) Search of
specific fragments that confirm chain composition (FA as M-H).
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as LPC only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idLPCneg(msobject)
## End(Not run)
Lysophosphocholines (LPC) annotation for ESI+
Description
LPC identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idLPCpos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H", "M+Na"),
clfrags = c(104.1075, 184.0739),
clrequired = c(F, F),
ftype = c("F", "F"),
chainfrags_sn1 = c("mg_M+H-H2O"),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for LPC in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments. See chainFrags for details. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idLPCpos
function involves 3 steps. 1) FullMS-based
identification of candidate LPC as M+H and M+Na. 2) Search of
LPC class fragments: 104.1075 and 184.0739 coeluting with the
precursor ion. 3) Search of specific fragments that confirm chain
composition (MG as M+H-H2O).
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as LPC only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idLPCpos(msobject)
## End(Not run)
Lysophosphoethanolamines (LPE) annotation for ESI-
Description
LPE identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idLPEneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M-H"),
clfrags = c(140.0115, 196.038, 214.048, "lysope_M-CH3"),
clrequired = c(F, F, F, "excluding"),
ftype = c("F", "F", "F", "BB"),
chainfrags_sn1 = c("fa_M-H"),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for LPE in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments. See chainFrags for details. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idLPEneg
function involves 3 steps. 1) FullMS-based
identification of candidate LPE as M-H. 2) Search of
LPE class fragments: 140.0115, 196.038 and 214.048 coeluting with the
precursor ion. If a loss of CH3 group is found coeluting with any candidate,
this will be excluded as it is a characteristic fragment of LPC.3) Search of
specific fragments that confirm chain composition (FA as M-H).
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as LPE only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idLPEneg(msobject)
## End(Not run)
Lysophosphoethanolamines (LPE) annotation for ESI+
Description
LPE identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idLPEpos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H", "M+Na"),
clfrags = c(141.01909),
clrequired = c(F),
ftype = c("NL"),
chainfrags_sn1 = c("mg_M+H-H2O"),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for LPE in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments. See chainFrags for details. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idLPEpos
function involves 3 steps. 1) FullMS-based
identification of candidate LPE as M+H and M+Na. 2) Search of
LPE class fragments: neutral loss of 141.01909 coeluting with the
precursor ion. 3) Search of specific fragments that confirm chain
composition in sn1 (MG as M+H-H2O).
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as LPE only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idLPEpos(msobject)
## End(Not run)
Lysophosphoglycerols (LPG) annotation for ESI-
Description
LPG identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idLPGneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M-H"),
clfrags = c(152.9958, 227.0326, 209.022, 74.0359),
clrequired = c(F, F, F, F),
ftype = c("F", "F", "F", "NL"),
chainfrags_sn1 = c("fa_M-H"),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for LPG in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments. See chainFrags for details. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idLPGneg
function involves 3 steps. 1) FullMS-based
identification of candidate LPG as M-H. 2) Search of LPG class fragments:
152.9958, 227.0326, 209.022 and neutral loss of 74.0359 coeluting with the
precursor ion. 3) Search of specific fragments that confirm chain composition
(FA as M-H).
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as LPG only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idLPGneg(msobject)
## End(Not run)
Lysophosphoinositols (LPI) annotation for ESI-
Description
LPI identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idLPIneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M-H"),
clfrags = c(241.0115, 223.0008, 259.0219, 297.0375),
clrequired = c(F, F, F, F),
ftype = c("F", "F", "F", "F"),
chainfrags_sn1 = c("fa_M-H"),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for LPI in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments. See chainFrags for details. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idLPIneg
function involves 3 steps. 1) FullMS-based
identification of candidate LPI as M-H. 2) Search of
LPI class fragments: 241.0115, 223.0008, 259.0219 and 297.0375 coeluting
with the precursor ion. 3) Search of specific fragments that confirm chain
composition (FA as M-H).
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as LPI only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idLPIneg(msobject)
## End(Not run)
Lysophosphoserines (LPS) annotation for ESI-
Description
LPS identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idLPSneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M-H", "M+Na-2H"),
clfrags = c(87.032),
clrequired = c(F),
ftype = c("NL"),
chainfrags_sn1 = c("fa_M-H"),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for LPS in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments. See chainFrags for details. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idLPSneg
function involves 3 steps. 1) FullMS-based
identification of candidate LPS as M-H and M+Na-2H. 2) Search of
LPS class fragments: neutral loss of 87.032 coeluting with the precursor ion.
3) Search of specific fragments that confirm chain composition (FA as M-H).
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as LPS only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idLPSneg(msobject)
## End(Not run)
Monoacylglycerol (MG) annotation for ESI+
Description
MG identification based on fragmentation patterns for LC-MS/MS DIA and DDA data acquired in positive mode.
Usage
idMGpos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H-H2O", "M+NH4", "M+Na"),
clfrags = c(),
clrequired = c(),
ftype = c(),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for MG in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idMGpos
function involves 2 steps. 1) FullMS-based
identification of candidate MG as M+H-H2O, M+NH4 and M+Na. 2) Search of
MG class fragments if any is assigned.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, just MS-only or Subclass level (if any class fragment is defined) are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idMGpos(msobject)
## End(Not run)
Lipids annotation for ESI-
Description
Lipids annotation based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode. This function compiles all functions writen for ESI- annotations.
Usage
idNEG(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 5,
coelCutoff = 0.8,
lipidClasses = c("FA", "FAHFA", "LPC", "LPE", "LPG", "LPI", "LPS", "PC", "PCo", "PCp",
"PE", "PEo", "PEp", "PG", "PI", "PS", "Sph", "SphP", "Cer", "CerP", "AcylCer", "SM",
"CL", "BA"),
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 5 seconds. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
lipidClasses |
classes of interest to run the identification functions. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification); and the annotatedPeaklist element shows the original MS1 peaklist with the annotations on it.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idNEG(msobject)
## End(Not run)
Phosphocholines (PC) annotation for ESI-
Description
PC identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idPCneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+CH3COO", "M-CH3", "M+CH3COO-CH3"),
clfrags = c(168.0426, 224.0688, "pc_M-CH3"),
clrequired = c(F, F, F),
ftype = c("F", "F", "BB"),
chainfrags_sn1 = c("lysopc_M-CH3"),
chainfrags_sn2 = c("fa_M-H", "lysopc_M-CH3"),
intrules = c("lysopc_sn1/lysopc_sn2"),
rates = c("3/1"),
intrequired = c(T),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for PC in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idPCneg
function involves 5 steps. 1) FullMS-based
identification of candidate PC as M+CH3COO, M-CH3 or M+CH3COO-CH3. To avoid
incorrect annotations of PE as PC, candidates which are present just as M-CH3
will be ignored. 2) Search of PC class fragments: 168.0426, 224.0688 or loss
of CH3 coeluting with the precursor ion. 3) Search of specific fragments that
inform about chain composition in sn1 (lysoPC as M-CH3 resulting from the
loss of the FA chain at sn2) and sn2 (lysoPC as M-CH3 resulting from the loss
of sn1 or FA as M-H). 4) Look for possible chains structure based on the
combination of chain fragments. 5) Check intensity rules to confirm chains
position. In this case, lysoPC from sn1 is at least 3 times more intense than
lysoPC from sn2.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idPCneg(msobject)
## End(Not run)
Plasmanyl Phosphocholines (PCo) annotation for ESI-
Description
PCo identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idPConeg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+CH3COO", "M-CH3", "M+CH3COO-CH3"),
clfrags = c(168.0426, 224.0688, "pco_M-CH3"),
clrequired = c(F, F, F),
ftype = c("F", "F", "BB"),
chainfrags_sn1 = c("lysopco_M-CH3", "lysopco_M-CH3-H2O"),
chainfrags_sn2 = c("fa_M-H", "fa_M-CO2-H"),
intrules = c("lysopco_sn1/fa_sn2"),
rates = c(1/3),
intrequired = c(T),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for PCo in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idPConeg
function involves 5 steps. 1) FullMS-based
identification of candidate PCo as M+CH3COO, M-CH3 or M+CH3COO-CH3. To avoid
incorrect annotations of PEo as PCo, candidates which are present just as M-CH3
will be ignored. 2) Search of PCo class fragments: 168.0426, 224.0688 or loss
of CH3 coeluting with the precursor ion. 3) Search of specific fragments that
inform about chain composition in sn1 (LPCo as M-CH3 and M-CH3-H2O resulting
from the loss of the FA chain at sn2) and sn2 (FA as M-H and M-CO2-H).
4) Look for possible chains structure based on the combination of chain
fragments. 5) Check intensity rules to confirm chains position. In this case,
FA fragments from sn2 are at least 3 times more intense than LPCo from sn1.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idPCneg(msobject)
## End(Not run)
Plasmanyl Phosphocholines (PCo) annotation for ESI+
Description
PCo identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idPCopos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H", "M+Na"),
clfrags = c(104.1075, 184.0739, 183.06604),
clrequired = c(F, F, F),
ftype = c("F", "F", "NL"),
chainfrags_sn1 = c("lysopco_M+H", "lysopco_M+H-H2O"),
chainfrags_sn2 = c("lysopc_M+H", "lysopc_M+H-H2O", ""),
intrules = c("lysopco_sn1/lysopc_sn2"),
rates = c("2/1"),
intrequired = c(T),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for PC in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idPCopos
function involves 5 steps. 1) FullMS-based
identification of candidate PCo as M+H and M+Na. 2) Search of PC class
fragments: 104.1075, 184.0739 and neutral loss of 183.06604 coeluting with
the precursor ion. 3) Search of specific fragments that inform about chain
composition in sn1 (LPCo as M+H or M+H-H2O resulting from the loss of the
FA chain at sn2) and sn2 (LPC as M+H-H2O resulting from the loss of
the FA chain at sn1 or the difference between precursor and sn1 chain
fragments). 4) Look for possible chains structure based on the combination of
chain fragments. 5) Check intensity rules to confirm chains position. In this
case, LPCo from sn1 is at least twice more intense than LPC from sn2.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idPCopos(msobject)
## End(Not run)
Plasmenyl Phosphocholines (PCp) annotation for ESI-
Description
PCp identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idPCpneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+CH3COO", "M-CH3", "M+CH3COO-CH3"),
clfrags = c(168.0426, 224.0688, "pcp_M-CH3"),
clrequired = c(F, F, F),
ftype = c("F", "F", "BB"),
chainfrags_sn1 = c("lysopcp_M-CH3", "lysopcp_M-CH3-H2O"),
chainfrags_sn2 = c("fa_M-H", "fa_M-CO2-H"),
intrules = c("lysopcp_sn1/fa_sn2"),
rates = c(1/3),
intrequired = c(T),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for PCp in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idPCpneg
function involves 5 steps. 1) FullMS-based
identification of candidate PCp as M+CH3COO, M-CH3 or M+CH3COO-CH3. To avoid
incorrect annotations of PEp as PCp, candidates which are present just as M-CH3
will be ignored. 2) Search of PCp class fragments: 168.0426, 224.0688 or loss
of CH3 coeluting with the precursor ion. 3) Search of specific fragments that
inform about chain composition in sn1 (LPCp as M-CH3 and M-CH3-H2O resulting
from the loss of the FA chain at sn2) and sn2 (FA as M-H and M-CO2-H).
4) Look for possible chains structure based on the combination of chain
fragments. 5) Check intensity rules to confirm chains position. In this case,
FA fragments from sn2 are at least 3 times more intense than LPCp from sn1.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idPCpneg(msobject)
## End(Not run)
Phosphocholines (PC) annotation for ESI+
Description
PC identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idPCpos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H", "M+Na"),
clfrags = c(104.1075, 184.0739, 183.06604),
clrequired = c(F, F, F),
ftype = c("F", "F", "NL"),
chainfrags_sn1 = c("lysopc_M+H", "lysopc_M+H-H2O"),
chainfrags_sn2 = c("lysopc_M+H", "lysopc_M+H-H2O", ""),
intrules = c("lysopc_sn1/lysopc_sn2"),
rates = c("2/1"),
intrequired = c(T),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for PC in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idPCpos
function involves 5 steps. 1) FullMS-based
identification of candidate PC as M+H and M+Na. 2) Search of PC class
fragments: 104.1075, 184.0739 and neutral loss of 183.06604 coeluting with
the precursor ion. 3) Search of specific fragments that inform about chain
composition in sn1 (lysoPC as M+H or M+H-H2O resulting from the loss of the
FA chain at sn2) and sn2 (lysoPC as M+H or M+H-H2O resulting from the loss of
the FA chain at sn1 or the difference between precursor and sn1 chain
fragments). 4) Look for possible chains structure based on the combination of
chain fragments. 5) Check intensity rules to confirm chains position. In this
case, lysoPC from sn1 is at least twice more intense than lysoPC from sn2.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idPCpos(msobject)
## End(Not run)
Plasmenyl Phosphocholines (PCp) annotation for ESI+
Description
PCp identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idPCppos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H", "M+Na"),
clfrags = c(104.1075, 184.0739, 183.06604),
clrequired = c(F, F, F),
ftype = c("F", "F", "NL"),
chainfrags_sn1 = c("lysopcp_M+H", "lysopcp_M+H-H2O"),
chainfrags_sn2 = c("lysopc_M+H-H2O", ""),
intrules = c("lysopcp_sn1/lysopc_sn2"),
rates = c("1/2"),
intrequired = c(T),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for PC in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idPCppos
function involves 5 steps. 1) FullMS-based
identification of candidate PC as M+H and M+Na. 2) Search of PC class
fragments: 104.1075, 184.0739 and neutral loss of 183.06604 coeluting with
the precursor ion. 3) Search of specific fragments that inform about chain
composition in sn1 (LPCp as M+H or M+H-H2O resulting from the loss of the
FA chain at sn2) and sn2 (LPC as M+H-H2O resulting from the loss of
the FA chain at sn1 or the difference between precursor and sn1 chain
fragments). 4) Look for possible chains structure based on the combination of
chain fragments. 5) Check intensity rules to confirm chains position. In this
case, LPC from sn2 is at least twice more intense than LPCo from sn1.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idPCppos(msobject)
## End(Not run)
Phosphoethanolamines (PE) annotation for ESI-
Description
PE identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idPEneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 5,
rt,
adducts = c("M-H"),
clfrags = c(140.0118, 196.038, 214.048, "pe_M-CH3"),
clrequired = c(F, F, F, "excluding"),
ftype = c("F", "F", "F", "BB"),
chainfrags_sn1 = c("lysope_M-H"),
chainfrags_sn2 = c("lysope_M-H", "fa_M-H"),
intrules = c("lysope_sn1/lysope_sn2"),
rates = c("3/1"),
intrequired = c(T),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for PE in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idPEneg
function involves 5 steps. 1) FullMS-based
identification of candidate PE as M-H. 2) Search of PE class fragments:
140.0115, 196.038, 214.048 ion coeluting with the precursor ion. If a loss of
CH3 group is found coeluting with any candidate, this will be excluded as it
is a characteristic fragment of PC. 3) Search of specific fragments that
inform about chain composition in sn1 (lysoPE as M-H resulting from the loss
of the FA chain at sn2) and sn2 (lysoPE as M-H resulting from the loss
of the FA chain at sn1 or FA chain as M-H). 4) Look for possible
chains structure based on the combination of chain fragments. 5) Check
intensity rules to confirm chains position. In this case, lysoPE from sn1 is
at least 3 times more intense than lysoPE from sn2.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idPEneg(msobject)
## End(Not run)
Plasmanyl Phosphoethanolamines (PEo) annotation for ESI-
Description
PEo identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idPEoneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 5,
rt,
adducts = c("M-H", "M+NaCH3COO"),
clfrags = c(140.0118, 196.038, 214.048, "peo_M-CH3"),
clrequired = c(F, F, F, "excluding"),
ftype = c("F", "F", "F", "BB"),
chainfrags_sn1 = c("lysopeo_M-H", "lysopeo_M-H-H2O"),
chainfrags_sn2 = c("fa_M-H"),
intrules = c("lysopeo_sn1/fa_sn2"),
rates = c(1/3),
intrequired = c(T),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for PEo in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idPEoneg
function involves 5 steps. 1) FullMS-based
identification of candidate PEo as M-H and M+NaCH3COO. 2) Search
of PEo class fragments: 140.0115, 196.038, 214.048 ion coeluting with the
precursor ion. If a loss of CH3 group is found coeluting with any candidate,
this will be excluded as it is a characteristic fragment of PCo. 3) Search of
specific fragments that inform about chain composition in sn1 (lysoPEo as M-H
and M-H-H2O resulting from the loss of the FA chain at sn2) and sn2 (FA chain
as M-H). 4) Look for possible chains structure based on the combination of
chain fragments. 5) Check intensity rules to confirm chains position. In this
case, FA fragments from sn2 are at least 3 times more intense than LPEo from
sn1.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idPEoneg(msobject)
## End(Not run)
Plasmanyl Phosphoethanolamines (PEo) annotation for ESI+
Description
PEo identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idPEopos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H", "M+Na"),
clfrags = c(141.0193),
clrequired = c(F),
ftype = c("NL"),
chainfrags_sn1 = c("lysopeo_M+H", "lysopeo_M+H-H2O"),
chainfrags_sn2 = c("mg_M+H-H2O"),
intrules = c("lysopeo_sn1/mg_sn2"),
rates = c("2/1"),
intrequired = c(T),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for PE in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idPEopos
function involves 5 steps. 1) FullMS-based
identification of candidate PE as M+H and M+Na. 2) Search of PE class
fragments: loss of head group (NL of 141.0193) coeluting with the precursor
ion. 3) Search of specific fragments that inform about chain composition at
sn1 (LPEo as M+H or M+H-H2O resulting from the loss of the FA chain at sn2)
and sn2 (MG as M+H-H2O resulting just from the loss of the head group and the
FA chain at sn1). 4) Look for possible chains structure based on the
combination of chain fragments. 5) Check intensity rules to confirm chains
position. LPEo from sn1 is at least 2 times more intense than MG from sn2.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idPEopos(msobject)
## End(Not run)
Plasmenyl Phosphoethanolamines (PEp) annotation for ESI-
Description
PEp identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idPEpneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 5,
rt,
adducts = c("M-H", "M+NaCH3COO"),
clfrags = c(140.0118, 196.038, 214.048, "pep_M-CH3"),
clrequired = c(F, F, F, "excluding"),
ftype = c("F", "F", "F", "BB"),
chainfrags_sn1 = c("lysopep_M-H", "lysopep_M-H-H2O"),
chainfrags_sn2 = c("fa_M-H"),
intrules = c("lysopep_sn1/fa_sn2"),
rates = c(1/3),
intrequired = c(T),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for PEp in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idPEpneg
function involves 5 steps. 1) FullMS-based
identification of candidate PEp as M-H and M+NaCH3COO. 2) Search
of PEp class fragments: 140.0115, 196.038, 214.048 ion coeluting with the
precursor ion. If a loss of CH3 group is found coeluting with any candidate,
this will be excluded as it is a characteristic fragment of PCp. 3) Search of
specific fragments that inform about chain composition in sn1 (lysoPEp as M-H
and M-H-H2O resulting from the loss of the FA chain at sn2) and sn2 (FA chain
as M-H). 4) Look for possible chains structure based on the combination of
chain fragments. 5) Check intensity rules to confirm chains position. In this
case, FA fragments from sn2 are at least 3 times more intense than LPEp from
sn1.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idPEoneg(msobject)
## End(Not run)
Phosphoethanolamines (PE) annotation for ESI+
Description
PE identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idPEpos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H", "M+Na"),
clfrags = c("dg_M+H-H2O"),
clrequired = c(F),
ftype = c("BB"),
chainfrags_sn1 = c("lysope_M+H-H2O", "mg_M+H-H2O"),
chainfrags_sn2 = c("mg_M+H-H2O"),
intrules = c("lysope_sn1/lysope_sn1", "mg_sn1/mg_sn2"),
rates = c("3/1", "1/2"),
intrequired = c(F, F),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for PE in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idPEpos
function involves 5 steps. 1) FullMS-based
identification of candidate PE as M+H and M+Na. 2) Search of PE class
fragments: loss of head group (DG as M+H-H2O) coeluting with the precursor
ion. 3) Search of specific fragments that inform about chain composition at
sn1 (MG as M+H-H2O resulting from the loss of the FA chain at sn2 and
the head group or LPE as M+H-H2O resulting just from the loss of the FA chain)
and sn2 (MG as M+H-H2O resulting from the loss of the head group and FA chain
from sn2). 4) Look for possible chains structure based on the combination of
chain fragments. 5) Check intensity rules to confirm chains position.
LPE or MG from sn1 is at least 3 times more intense than the ones from sn2.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idPEpos(msobject)
## End(Not run)
Plasmenyl Phosphoethanolamines (PEp) annotation for ESI+
Description
PEp identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idPEppos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H", "M+Na"),
clfrags = c(140.012),
clrequired = c(F),
ftype = c("NL"),
chainfrags_sn1 = c("lysopep_M+H", "lysopep_M+H-H2O"),
chainfrags_sn2 = c("mg_M+H-H2O"),
intrules = c("lysopep_sn1/mg_sn2"),
rates = c("1/3"),
intrequired = c(T),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for PE in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idPEppos
function involves 5 steps. 1) FullMS-based
identification of candidate PE as M+H and M+Na. 2) Search of PE class
fragments: loss of head group (NL of 140.012) coeluting with the precursor
ion. 3) Search of specific fragments that inform about chain composition at
sn1 (LPEp as M+H or M+H-H2O resulting from the loss of the FA chain at sn2)
and sn2 (MG as M+H-H2O from sn2 resulting from the loss of the FA chain at sn1).
4) Look for possible chains structure based on the combination of chain
fragments. 5) Check intensity rules to confirm chains position. MG from sn2
is at least 3 times more intense than LPEp from sn1.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idPEppos(msobject)
## End(Not run)
Phosphoglycerols (PG) annotation for ESI-
Description
PG identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idPGneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M-H"),
clfrags = c(152.9958, 227.0326, 209.022, 74.0359),
clrequired = c(F, F, F, F),
ftype = c("F", "F", "F", "NL"),
chainfrags_sn1 = c("lysopg_M-H"),
chainfrags_sn2 = c("lysopg_M-H", "fa_M-H"),
intrules = c("lysopg_sn1/lysopg_sn2"),
rates = c("2/1"),
intrequired = c(T),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for PG in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idPGneg
function involves 5 steps. 1) FullMS-based
identification of candidate PG as M-H. 2) Search of PG class fragments:
152.9958, 227.0326, 209.022 and neutral loss of 74.0359 coeluting with the
precursor ion. 3) Search of specific fragments that inform about chain
composition at sn1 (lysoPG as M-H resulting from the loss of the FA chain
at sn2) and sn2 (lysoPG as M-H resulting from the loss of the FA chain
at sn1 or FA chain as M-H). 4) Look for possible chains structure
based on the combination of chain fragments. 5) Check intensity rules to
confirm chains position. In this case, lysoPG from sn1 is at least 3 times
more intense than lysoPG from sn2.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idPGneg(msobject)
## End(Not run)
Phosphoglycerols (PG) annotation for ESI+
Description
PG identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idPGpos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H", "M+NH4", "M+Na"),
clfrags = c("dg_M+H-H2O"),
clrequired = c(F),
ftype = c("BB"),
chainfrags_sn1 = c("mg_M+H-H2O"),
chainfrags_sn2 = c("mg_M+H-H2O"),
intrules = c("mg_sn1/mg_sn2"),
rates = c("1/2"),
intrequired = c(F),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for PE in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idPGpos
function involves 5 steps. 1) FullMS-based
identification of candidate PG as M+H, M+NH4 and M+Na. 2) Search of PG class
fragments: loss of head group (DG as M+H-H2O) coeluting with the precursor
ion. 3) Search of specific fragments that inform about chain composition at
sn1 (MG as M+H-H2O resulting from the loss of the FA chain at sn2)
and sn2 (MG as M+H-H2O resulting from the loss of the FA chain at sn1).
4) Look for possible chains structure based on the combination of chain
fragments. 5) Check intensity rules to confirm chains position. MG from sn2
is at least twice more intense than the one from sn1.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idPGpos(msobject)
## End(Not run)
Phosphoinositols (PI) annotation for ESI-
Description
PI identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idPIneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M-H"),
clfrags = c(241.0115, 223.0008, 259.0219, 297.0375),
clrequired = c(F, F, F, F),
ftype = c("F", "F", "F", "F"),
chainfrags_sn1 = c("lysopi_M-H", "lysopa_M-H"),
chainfrags_sn2 = c("lysopi_M-H", "lysopa_M-H", "fa_M-H"),
intrules = c("lysopi_sn1/lysopi_sn2", "lysopa_sn1/lysopa_sn2"),
rates = c("3/1", "3/1"),
intrequired = c(F, F),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for PI in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idPIneg
function involves 5 steps. 1) FullMS-based
identification of candidate PI as M-H. 2) Search of PI class fragments:
241.0115, 223.0008, 259.0219 and 297.0375 coeluting with the precursor
ion. 3) Search of specific fragments that inform about chain composition at
sn1 (lysoPI as M-H resulting from the loss of the FA chain at sn2 or lysoPA
as M-H if it also losses the head group) and sn2 (lysoPI or lysoPA as M-H
resulting from the loss of the FA chain at sn1 or FA chain as M-H). 4) Look
for possible chains structure based on the combination of chain fragments.
5) Check intensity rules to confirm chains position. In this case, lysoPI or
lysoPA from sn1 is at least 3 times more intense than lysoPI or lysoPA from
sn2.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idPIneg(msobject)
## End(Not run)
Phosphoinositols (PI) annotation for ESI+
Description
PI identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idPIpos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H", "M+NH4", "M+Na"),
clfrags = c("dg_M+H-H2O"),
clrequired = c(F),
ftype = c("BB"),
chainfrags_sn1 = c("mg_M+H-H2O", "lysopi_M+H-H2O"),
chainfrags_sn2 = c("mg_M+H-H2O", "lysopi_M+H-H2O"),
intrules = c("mg_sn1/mg_sn2", "lysopi_sn1/lysopi_sn2"),
rates = c("2/1", "2/1"),
intrequired = c(F, F),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for PE in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idPIpos
function involves 5 steps. 1) FullMS-based
identification of candidate PI as M+H, M+NH4 and M+Na. 2) Search of PI class
fragments: loss of head group (DG as M+H-H2O) coeluting with the precursor
ion. 3) Search of specific fragments that inform about chain composition at
sn1 (MG as M+H-H2O or LPI as M+H-H2O resulting from the loss of the FA chain
at sn2) and sn2 (MG as M+H-H2O or LPI as M+H-H2O resulting from the loss of
the FA chain at sn1). 4) Look for possible chains structure based on the
combination of chain fragments. 5) Check intensity rules to confirm chains
position. MG or LPI from sn1 are at least twice more intense than the ones
from sn2.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idPIpos(msobject)
## End(Not run)
Lipids annotation for ESI+
Description
Lipids annotation based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode. This function compiles all functions written for ESI+ annotations.
Usage
idPOS(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 5,
coelCutoff = 0.8,
lipidClasses = c("MG", "LPC", "LPE", "PC", "PCo", "PCp", "PE", "PEo", "PEp", "PG",
"PI", "Sph", "SphP", "Cer", "AcylCer", "CerP", "SM", "Carnitines", "CE", "DG", "TG"),
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 5 seconds. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
lipidClasses |
classes of interest to run the identification functions. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification); and the annotatedPeaklist element shows the original MS1 peaklist with the annotations on it.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idPOS(msobject)
## End(Not run)
Phosphoserines (PS) annotation for ESI-
Description
PS identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idPSneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M-H", "M+Na-2H"),
clfrags = c(87.032, 152.9958),
clrequired = c(F, F),
ftype = c("NL", "F"),
chainfrags_sn1 = c("lysopa_M-H", "lysopa_M-H-H2O"),
chainfrags_sn2 = c("lysopa_M-H", "lysopa_M-H-H2O", "fa_M-H"),
intrules = c("lysopa_sn1/lysopa_sn2"),
rates = c("3/1"),
intrequired = c(T),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for PS in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idPSneg
function involves 5 steps. 1) FullMS-based
identification of candidate PS as M-H or M+Na-2H. 2) Search of PS class
fragments: neutral loss of 87.032 (serine) coeluting with the precursor ion.
3) Search of specific fragments that inform about chain composition at sn1
(lysoPA as M-H or M-H-H2O resulting from the loss of the FA chain at sn2 and
the head group) and sn2 (lysoPA as M-H or M-H-H2O resulting from the loss of
the FA chain at sn1 and the head group or FA chain as M-H). 4) Look for
possible chains structure based on the combination of chain fragments.
5) Check intensity rules to confirm chains position. In this case, lysoPA
from sn1 is at least 3 times more intense than lysoPA from sn2.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idPSneg(msobject)
## End(Not run)
Sphingomyelines (SM) annotation for ESI-
Description
SM identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idSMneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+CH3COO", "M-CH3", "M+CH3COO-CH3"),
clfrags = c(168.0426, 224.0688, "sm_M-CH3"),
clrequired = c(F, F, F),
ftype = c("F", "F", "BB"),
chainfrags_sn1 = c("sph_Mn+150.032"),
chainfrags_sn2 = c("fa_Mn-1.9918", ""),
intrules = c(),
rates = c(),
intrequired = c(),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for PC in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idSMneg
function involves 5 steps. 1) FullMS-based
identification of candidate SM as M+CH3COO, M-CH3 or M+CH3COO-CH3. 2) Search
of SM class fragments: 168.0426, 224.0688 or loss of CH3 coeluting with the
precursor ion. 3) Search of specific fragments that inform about chain
composition in sn1 (Sph+phosphocholine as M-CH3-H2O which results in a mass
difference of Sph+150.032) and sn2 (difference between precursor and sn1 chain
fragments). 4) Look for possible chains structure based on the
combination of chain fragments. 5) Check intensity rules to confirm chains
position. In this case, there are no intensity rules by default.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idSMneg(msobject)
## End(Not run)
Sphyngomyelines (SM) annotation for ESI+
Description
SM identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idSMpos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H", "M+Na"),
clfrags = c(104.1075, 184.0739, 183.06604),
clrequired = c(F, F, F),
ftype = c("F", "F", "NL"),
chainfrags_sn1 = c("sph_M+H-2H2O"),
chainfrags_sn2 = c(""),
intrules = c(),
rates = c(),
intrequired = c(),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for SM in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idSMpos
function involves 5 steps. 1) FullMS-based
identification of candidate SM as M+H and M+Na. 2) Search of SM class
fragments: 104.1075, 184.0739 and neutral loss of 183.06604 coeluting with
the precursor ion. 3) Search of specific fragments that inform about the
composition of the sphingoid base (Sph as M+H-2H2O resulting from the loss of
the FA chain) and the FA chain (by default it is calculated using the
difference between precursor and sph chain fragments). 4) Look for possible
chains structure based on the combination of chain fragments. 5) Check
intensity rules to confirm chains position. In this case, there are no
intensity rules by default as FA chain is unlikely to be detected.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idSMpos(msobject)
## End(Not run)
Sphingoid bases phosphate (SphP) annotation for ESI-
Description
SphP identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idSphPneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M-H"),
clfrags = c(78.9585, 96.9691, "sphP_M-H-H2O"),
clrequired = c(F, F, F),
ftype = c("F", "F", "BB"),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for SphP in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idSphpos
function involves 2 steps. 1) FullMS-based
identification of candidate SphP as M-H. 2) Search of SphP class fragments:
78.9585, 96.969 or neutral loss of 1 H2O molecule.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as SphP only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idSphPneg(msobject)
## End(Not run)
Sphingoid bases phosphate (SphP) annotation for ESI+
Description
SphP identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idSphPpos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H"),
clfrags = c("sphP_M+H-H2O", "sphP_M+H-2H2O", "sphP_M+H-H2O-NH4"),
clrequired = c(F, F, F),
ftype = c("BB", "BB", "BB"),
coelCutoff = 0.7,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursors and product ions. By default, 3 seconds. |
rt |
rt window where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for Sph in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idSphPpos
function involves 2 steps. 1) FullMS-based
identification of candidate SphP as M+H. 2) Search of SphP class fragments:
neutral loss of 1 or 2 H2O molecules, or H2O and NH4.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as SphP only have one chain, only Subclass and FA level are possible). and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idSphPpos(msobject)
## End(Not run)
Sphingoid bases (Sph) annotation for ESI-
Description
Sph identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in negative mode.
Usage
idSphneg(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M-H"),
clfrags = c("sph_M-H-H2O", "sph_M-H-2H2O"),
clrequired = c(F, F),
ftype = c("BB", "BB"),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for Sph in ESI-. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idSphneg
function involves 2 steps. 1) FullMS-based
identification of candidate Sph as M-H. 2) Search of Sph class fragments:
neutral loss of 1 or 2 H2O molecules.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as Sph only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been writen based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Examples
## Not run:
msobject <- idSphneg(msobject)
## End(Not run)
Sphingoid bases (Sph) annotation for ESI-
Description
Sph identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idSphpos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+H"),
clfrags = c("sph_M+H-H2O", "sph_M+H-2H2O"),
clrequired = c(F, F),
ftype = c("BB", "BB"),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursors and product ions. By default, 3 seconds. |
rt |
rt window where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for Sph in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idSphpos
function involves 2 steps. 1) FullMS-based
identification of candidate Sph as M+H. 2) Search of Sph class fragments:
neutral loss of 1 or 2 H2O molecules.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (in this case, as Sph only have one chain, only Subclass and FA level are possible) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idSphpos(msobject)
## End(Not run)
Triacylglycerols (TG) annotation for ESI+
Description
TG identification based on fragmentation patterns for LC-MS/MS DIA or DDA data acquired in positive mode.
Usage
idTGpos(
msobject,
ppm_precursor = 5,
ppm_products = 10,
rttol = 3,
rt,
adducts = c("M+NH4", "M+Na"),
clfrags = c(),
clrequired = c(),
ftype = c(),
chainfrags_sn1 = c("cbdiff-dg_M+H-H2O"),
chainfrags_sn2 = c("cbdiff-dg_M+H-H2O"),
chainfrags_sn3 = c("cbdiff-dg_M+H-H2O"),
intrules = c("cbdiff-dg_sn2/cbdiff-dg_sn1", "cbdiff-dg_sn2/cbdiff-dg_sn3",
"cbdiff-dg_sn1/cbdiff-dg_sn3"),
rates = c("1", "1", "1"),
intrequired = c(T, T, T),
coelCutoff = 0.8,
dbs,
verbose = TRUE
)
Arguments
msobject |
an msobject returned by dataProcessing. |
ppm_precursor |
mass tolerance for precursor ions. By default, 5 ppm. |
ppm_products |
mass tolerance for product ions. By default, 10 ppm. |
rttol |
total rt window for coelution between precursor and product ions. By default, 3 seconds. |
rt |
rt range where the function will look for candidates. By default, it will search within all RT range in MS1. |
adducts |
expected adducts for TG in ESI+. Adducts allowed can be modified in adductsTable (dbs argument). |
clfrags |
vector containing the expected fragments for a given lipid class. See checkClass for details. |
clrequired |
logical vector indicating if each class fragment is required or not. If any of them is required, at least one of them must be present within the coeluting fragments. See checkClass for details. |
ftype |
character vector indicating the type of fragments in clfrags. It can be: "F" (fragment), "NL" (neutral loss) or "BB" (building block). See checkClass for details. |
chainfrags_sn1 |
character vector containing the fragmentation rules for the chain fragments in sn1 position. See chainFrags for details. |
chainfrags_sn2 |
character vector containing the fragmentation rules for the chain fragments in sn2 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn1 chains. |
chainfrags_sn3 |
character vector containing the fragmentation rules for the chain fragments in sn3 position. See chainFrags for details. If empty, it will be estimated based on the difference between precursors and sn2 chains. |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. If some intensity rules should be employed to identify the chains position but they are't known yet, use "Unknown". If it isn't required, leave an empty vector. |
rates |
character vector with the expected rates between fragments given as a string (e.g. "3/1"). See checkIntensityRules. |
intrequired |
logical vector indicating if any of the rules is required. If not, at least one must be verified to confirm the structure. |
coelCutoff |
coelution score threshold between parent and fragment ions. Only applied if rawData info is supplied. By default, 0.8. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
verbose |
print information messages. |
Details
idTGpos
function involves 5 steps. 1) FullMS-based
identification of candidate TG as M+NH4 and M+Na. 2) Search of TG class
fragments: there are no class fragment by default. 3) Search of specific
fragments that inform about the FA chains: DGs resulting from the loss of FA
chains as M+H-H2O. 4) Look for possible chains structure based on the
combination of chain fragments. 5) Check intensity rules to confirm chains
position. In the case of TG, DG resulting from the loss of sn2 if the most
intense, followed by the loss of sn1 and sn3, but this FA position level
still needs to be improved due to the high level of coelution for TG.
Results data frame shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity, which comes directly from de input), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level) and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Value
annotated msobject (list with several elements). The results element is a data frame that shows: ID, lipid class, CDB (total number of carbons and double bounds), FA composition (specific chains composition if it has been confirmed), mz, RT (in seconds), I (intensity), Adducts, ppm (mz error), confidenceLevel (Subclass, FA level, where chains are known but not their positions, or FA position level), peakID, and Score (parent-fragment coelution score mean in DIA data or relative sum intensity in DDA of all fragments used for the identification).
Note
This function has been written based on fragmentation patterns observed for three different platforms (QTOF 6550 from Agilent, Synapt G2-Si from Waters and Q-exactive from Thermo), but it may need to be customized for other platforms or acquisition settings.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msobject <- idTGpos(msobject)
## End(Not run)
Index partitions or clusters assigned during alignment.
Description
Index partitions or clusters assigned during alignment.
Usage
indexrtpart(peaks, part, minsamples)
Arguments
peaks |
peaks obtained using getallpeaks. |
part |
numeric vector with the partition/cluster assigned to each peak. |
minsamples |
minimum number of samples represented in each partition. |
Value
list with two elements: index and vector with the assigned partitions.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@iislafe.es>
Summarize annotation results from an msbatch into the features table
Description
Summarize annotation results from an msbatch into the features table
Usage
joinAnnotationResults(msbatch, simplifyAnnotations = TRUE)
Arguments
msbatch |
msbatch |
simplifyAnnotations |
logical. If TRUE, only the most frequent id will be kept (recommended when only pool samples have been acquired in DIA or DDA). If FALSE, all annotations will be shown. |
Value
msbatch
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Join fragments information when several peaks of the same fragment are coeluting with a unique candidate
Description
Function employed by frags.
Usage
joinfrags(df)
Arguments
df |
data frame containing coeluting fragments |
Value
Data frame
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
LPAs database
Description
In silico generated database for common LPAs.
Usage
data("lysopadb")
Format
Data frame with 30 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
O-LPA database
Description
In silico generated database for common O-LPA.
Usage
data("lysopaodb")
Format
Data frame with 30 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
LPCs database
Description
In silico generated database for common LPCs.
Usage
data("lysopcdb")
Format
Data frame with 30 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
O-LPC database
Description
In silico generated database for common O-LPC.
Usage
data("lysopcodb")
Format
Data frame with 30 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
P-LPC database
Description
In silico generated database for common P-LPC.
Usage
data("lysopcpdb")
Format
Data frame with 30 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
LPEs database
Description
In silico generated database for common LPEs.
Usage
data("lysopedb")
Format
Data frame with 30 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
O-LPE database
Description
In silico generated database for common O-LPE.
Usage
data("lysopeodb")
Format
Data frame with 30 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
P-LPE database
Description
In silico generated database for common P-LPE.
Usage
data("lysopepdb")
Format
Data frame with 30 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
LPGs database
Description
In silico generated database for common LPGs.
Usage
data("lysopgdb")
Format
Data frame with 30 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
LPIs database
Description
In silico generated database for common LPIs.
Usage
data("lysopidb")
Format
Data frame with 30 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
LPSs database
Description
In silico generated database for common LPSs
Usage
data("lysopsdb")
Format
Data frame with 30 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
MGs database
Description
In silico generated database for common MGs.
Usage
data("mgdb")
Format
Data frame with 30 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
mz match withing a vector of mz values
Description
This function searches marches between a given mz and a vector of mz values with certain mass tolerance and returns the index of the matched values. It is used by identification functions to find candidates of each class of lipid based on full MS information.
Usage
mzMatch(mz, mzvector, ppm)
Arguments
mz |
mz value to be matched |
mzvector |
vector of mz values |
ppm |
mass error tolerance |
Value
Numeric vector indicating the index of matched mz values and ppms for each one of those matches (match1, ppm1, match2, ppm2, etc.)
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Neutral losses db for sphingoid bases. It is employed by idCerneg function.
Description
In silico generated database for neutral losses of sphingoid bases in ESI-.
Usage
data("nlsphdb")
Format
Data frame with 4 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
Prepare output for LipidMS annotation functions
Description
Prepare a readable output for LipidMS identification functions.
Usage
organizeResults(
candidates,
coelfrags,
clfrags,
classConf,
chainsComb,
intrules,
intConf,
nchains,
class,
acquisitionmode
)
Arguments
candidates |
candidates data frame. Output of findCandidates. |
coelfrags |
list of coeluting fragments for each candidate |
clfrags |
vector containing the expected fragments for a given lipid class. |
classConf |
output of checkClass |
chainsComb |
output of combineChains |
intrules |
character vector specifying the fragments to compare. See checkIntensityRules. |
intConf |
output of checkIntensityRules |
nchains |
number of chains of the targeted lipid class. |
class |
character value. Lipid class (i.e. PC, PE, DG, TG, etc.). |
acquisitionmode |
acquisition mode (DIA or DDA). |
Details
Coelution score for DIA data is calculated as the mean coelution score of all fragments used for annotation, while for DDA data, the intensity score is given, which is calculated as the sum of the relative intensities of the fragments used for annotation.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
PAs database
Description
In silico generated database for common PAs.
Usage
data("padb")
Format
Data frame with 147 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
agglomarative partitioning for LC-HRMS data based on enviPick algorithm
Description
agglomarative partitioning for LC-HRMS data based on enviPick algorithm
Usage
partitioning(msobject, dmzagglom, drtagglom, minpeak, mslevel, cE)
Arguments
msobject |
msobject generated by readMSfile |
dmzagglom |
mz tolerance for partitions |
drtagglom |
RT window for partitions |
minpeak |
minimum number of measures to define a peak |
mslevel |
MS level information to access msobject |
cE |
collision energy information to access msobject |
Value
msobject
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
References
Peak-picking algorithm has been imported from enviPick R-package: https://cran.r-project.org/web/packages/enviPick/index.html
PCs database
Description
In silico generated database for common PCs.
Usage
data("pcdb")
Format
Data frame with 147 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
O-PC database
Description
In silico generated database for common O-PC.
Usage
data("pcodb")
Format
Data frame with 147 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
P-PC database
Description
In silico generated database for common P-PC.
Usage
data("pcpdb")
Format
Data frame with 147 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
peak-pick based on previous EIC clusters generated by clustering
Description
peak-pick based on previous EIC clusters generated by clustering
Usage
peakdetection(
msobject,
minpeak,
drtminpeak,
drtmaxpeak,
drtgap,
recurs,
weight,
ended,
sb,
sn,
minint,
mslevel,
cE
)
Arguments
msobject |
msobject generated by clustering |
minpeak |
minimum number of measures to define a peak |
drtminpeak |
minimum RT length of a peak |
drtmaxpeak |
maximum RT length of a peak |
drtgap |
maximum RT gap to be filled |
recurs |
maximum number of peaks for a EIC |
weight |
weight for assigning measurements to a peak |
ended |
number of failures allowed when detecting peaks |
sb |
signal-to-base ration |
sn |
signal-to-noise ratio |
minint |
minimum intensity |
mslevel |
info to access msobject |
cE |
info to access msobject |
Value
msobject
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
References
Peak-picking algorithm has been imported from enviPick R-package: https://cran.r-project.org/web/packages/enviPick/index.html
PEs database
Description
In silico generated database for common PEs.
Usage
data("pedb")
Format
Data frame with 147 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
O-PE database
Description
In silico generated database for common O-PE.
Usage
data("peodb")
Format
Data frame with 147 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
P-PE database
Description
In silico generated database for common P-PE.
Usage
data("pepdb")
Format
Data frame with 147 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
PGs database
Description
In silico generated database for common PGs.
Usage
data("pgdb")
Format
Data frame with 147 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
PIs database
Description
In silico generated database for common PIs.
Usage
data("pidb")
Format
Data frame with 147 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
Plot informative peaks for lipid annotation
Description
Plot informative peaks for each lipid annotated with idPOS and idNEG (or similar functions).
Usage
plotLipids(msobject, span = 0.4, ppm = 10, verbose = TRUE)
Arguments
msobject |
annotated msobject. |
span |
smoothing parameter. Numeric value between 0 and 1. |
ppm |
mz tolerance for EIC. If set to 0, the EIC will not be shown. |
verbose |
print information messages. |
Details
Peak intensities are relative to the maximum intensity of each peak to ease visualization.
Grey lines show the the extracted ion chromatograms for the peaks.
Value
msobject with a plots element which contains a list of plots. Plots on the left side represent raw values while plots on the left are smoothed or clean scans (MS2 in DDA).
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
EIC for all samples in a msbatch
Description
EIC for all samples in a msbatch
Usage
ploteicmsbatch(msbatch, mz, ppm, rt, colorbygroup = TRUE, verbose = TRUE)
Arguments
msbatch |
msbatch |
mz |
mz of interest |
ppm |
mass tolerance in ppm |
rt |
numeric vector with the RT range to be plotted |
colorbygroup |
logical. If TRUE, samples will be coloured based on their sample group (from metadata). |
verbose |
print information messages. |
Value
plot
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
TIC for all samples in a msbatch
Description
TIC for all samples in a msbatch
Usage
plotticmsbatch(msbatch, rt, colorbygroup = TRUE)
Arguments
msbatch |
msbatch |
rt |
numeric vector with the RT range to be plotted |
colorbygroup |
logical. If TRUE, samples will be coloured based on their sample group (from metadata). |
Value
plot
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
PSs database
Description
In silico generated database for common PSs.
Usage
data("psdb")
Format
Data frame with 147 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
Read mzXML file and initiate msobject
Description
Read mzXML file and initiate msobject
Usage
readMSfile(file, polarity)
Arguments
file |
file path for a mzXML file |
polarity |
character value: negative or positive. |
Value
msobject
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Correct RT based on a rtmodel.
Description
Correct RT based on a rtmodel.
Usage
rtcorrection(rt, rtmodel)
Arguments
rt |
rt vector to correct. |
rtmodel |
rt model. |
Value
corrected rt vector.
Author(s)
M Isabel Alcoriza-Balaguer <maialba@iislafe.es>
Plot retention time deviation
Description
Plot retention time deviation of an aligned msbatch
Usage
rtdevplot(msbatch, colorbygroup = TRUE)
Arguments
msbatch |
aligned msbatch. |
colorbygroup |
logical. If TRUE, samples will be coloured based on their sample group (from metadata). |
Value
plot
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Targeted isotopes search
Description
This function uses annotation results of deisotoped data to search for isotopes in raw data.
Usage
searchIsotopes(
msobject,
label,
adductsTable = LipidMS::adductsTable,
ppm = 10,
coelCutoff = 0.7,
results,
dbs
)
Arguments
msobject |
msobject. |
label |
isotope employed for the experiment. It can be "13C" or "D". |
adductsTable |
adducts table employed for lipids annotation. |
ppm |
mass error tolerance. |
coelCutoff |
coelution score threshold between isotopes. By default, 0.7. |
results |
target list to search isotopes. If missing, all results from the msobject are searched. It is used by searchIsotopesmsbatch. |
dbs |
list of data bases required for annotation. By default, dbs contains the required data frames based on the default fragmentation rules. If these rules are modified, dbs may need to be supplied. See createLipidDB and assignDB. |
Value
List with the isotopes for each compound in the results data frame.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Targeted isotopes search for msbatch
Description
This function uses annotation results of deisotoped data to search for isotopes in raw data.
Usage
searchIsotopesmsbatch(
msbatch,
label,
adductsTable = LipidMS::adductsTable,
ppm = 10,
coelCutoff = 0.7
)
Arguments
msbatch |
annotated msbatch. |
label |
isotope employed for the experiment. It can be "13C" or "D". |
adductsTable |
adducts table employed for lipids annotation. |
ppm |
mass error tolerance. |
coelCutoff |
coelution score threshold between isotopes. By default, 0.7. |
Value
List with the isotopes for each compound in the results data frame.
Author(s)
M Isabel Alcoriza-Balaguer <maribel_alcoriza@iislafe.es>
Examples
## Not run:
msbatch <- batchProcessing(metadata = "metadata.csv", polarity = "positive")
msbatch <- alignmsbatch(msbatch)
msbatch <- groupmsbatch(msbatch)
msbatch <- annotatemsbatch(msbatch)
searchIsotopesmsbatch(msbatch, label = "13C")
## End(Not run)
Check matches between chains composition and precursor structures
Description
This function checks if the sum up of the chains structure match the precursor structure. It is used by combineChains.
Usage
select(chains, parent, n)
Arguments
chains |
character value containing chains structure separated by a white space. |
parent |
precursor ion structure |
n |
number of chains |
Value
Logical value
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
Create msbatch for batch processing.
Description
Create msbatch from a list of msobjects to build an msbatch.
Usage
setmsbatch(msobjectlist, metadata)
Arguments
msobjectlist |
list of msobjects. |
metadata |
sample metadata. Optional. It can be a csv file or a data.frame with 3 columns (sample, acquistionmode and sampletype). |
Details
samples are sorted following the metadata data.frame.
Value
msbatch
Author(s)
M Isabel Alcoriza-Balaguer <maialba@iislafe.es>
See Also
dataProcessing and batchdataProcessing
Examples
## Not run:
msbatch <- setmsbatch(msobjectlist)
## End(Not run)
SMs database
Description
In silico generated database for common SMs.
Usage
data("smdb")
Format
Data frame with 52 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
Sphingoid bases phosphate database
Description
In silico generated database for common sphingoid bases phosphate.
Usage
data("sphPdb")
Format
Data frame with 4 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
Sphingoid bases database
Description
In silico generated database for common sphingoid bases.
Usage
data("sphdb")
Format
Data frame with 4 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.
Calculate total number of carbons and double bounds of lipid chains
Description
Given the structure of a lipid specie, it sums up the chains.
Usage
sumChains(chains, n)
Arguments
chains |
character value with the configuration of the chains separated by a white space |
n |
number of chains |
Value
Character value indicating the total number of carbons and double bounds
Author(s)
M Isabel Alcoriza-Balaguer <maialba@alumni.uv.es>
TGs database
Description
In silico generated database for common TGs.
Usage
data("tgdb")
Format
Data frame with 376 observations and the following 3 variables.
formula
character vector containing molecular formulas.
total
character vector indicating the total number of carbons and double bounds of the chains.
Mass
numeric vector with the neutral masses.