PTXQC: A package for computing Quality Control (QC) metrics for Proteomics (PTX)

Description

The following sections describe the main components:

Input

Valid input data are either the files from MaxQuant's .txt folder (all versions from MaxQuant >= 1.0 upwards are supported) or a single mzTab file. All mzTab files will work, but most metrics can be obtained from OpenMS' mzTab as produced by the QualityControl TOPP tool (from OpenMS 2.5 onwards).

Important functions

The central function of this package is called createReport and it accepts either MaxQuant or mzTab data, along with a configuration (optional). There is a parser for mzTab MzTabReader and MaxQuant txt files MQDataReader, as well as a plethora of QC metrics derived from a common qcMetric class and scoring functions qual..., e.g. qualGaussDev.

Configuration

The user can modify the behaviour of PTXQC, e.g. to enable/disable certain metrics or change scoring thresholds, via a YAML object/file. By default a Yaml file is written automatically side-by-side to the input files upon running PTXQC for the first time on a particular input. A custom Yaml object can be passed to the main createReport function for customization. Use yaml::yaml.load_file(input = 'myYAML.yaml') to load an existing file and pass the Yaml object along.

Output

Either a PDF and/or Html report which contains QC plots and a description of the metrics.

Author(s)

Maintainer: Chris Bielow chris.bielow@bsc.fu-berlin.de

Other contributors:

• Juliane Schmachtenberg [contributor]

• Swenja Wagner [contributor]

• Patricia Scheil [contributor]

• Tom Waschischek [contributor]

• Guido Mastrobuoni [data contributor, reviewer]

See Also

Useful links:

• https://github.com/cbielow/PTXQC

• Report bugs at https://github.com/cbielow/PTXQC/issues

A string concatenation function, more readable than 'paste()'.

Description

A string concatenation function, more readable than 'paste()'.

Usage

a %+% b

Arguments

Value

Concatenated string (no separator)

Coefficient of variation (CV)

Description

Computes sd(x) / mean(x)

Usage

CV(x)

Arguments

Value

CV

Make sure to call $readMappingFile(some_file) if you want to support a user-defined file mapping. Otherwise, calls to $getShortNames() will create/augment the mapping for filenames.

Description

Make sure to call $readMappingFile(some_file) if you want to support a user-defined file mapping. Otherwise, calls to $getShortNames() will create/augment the mapping for filenames.

Fields

raw_file_mapping

Data.frame with columns 'from', 'to' and maybe 'best.effort' (if shorting was unsuccessful)

mapping.creation

how the current mapping was obtained (user or auto)

external.mapping.file

Filename of user-defined mapping file; only defined if readMappingFile() was called

Methods

getShortNamesStatic(raw.files, max_len, fallbackStartNr = 1)

Static method: Shorten a set of Raw file names and return a data frame with the mappings. Mapping will have: $from, $to and optionally $best.effort (if shorting was unsuccessful and numbers had to be used)

• ⁠raw.files⁠ Vector of Raw files.

• ⁠max_len⁠ Maximal length of shortening results, before resorting to canonical names (file 1,...).

• ⁠fallbackStartNr⁠ Starting index for canonical names.

Return Value

data.frame with mapping.

Examples

a = FilenameMapper$new()
a$readMappingFile('filenamemapping.txt') 

Compute longest common substring of two strings.

Description

Implementation is very inefficient (dynamic programming in R) –> use only on small instances

Usage

LCS(s1, s2)

Arguments

Value

String containing the longest common substring

Find longest common substring from 'n' strings.

Description

Warning: greedy heuristic! This is not guaranteed to find the best solution (or any solution at all), since its done pairwise with the shortest input string as reference.

Usage

LCSn(strings, min_LCS_length = 0)

Arguments

Value

longest common substring (or "" if shorter than min_LCS_length)

Examples

LCSn(c("1_abcde...",
       "2_abcd...",
       "x_abc..."))  ## --> "_abc"
LCSn(c("16_IMU008_CISPLA_E5_R11", 
       "48_IMU008_CISPLA_P4_E7_R31",
       "60_IMU008_CISPLA_E7_R11"), 3) ## -->"_IMU008_CISPLA_"
LCSn(c("AAAAACBBBBB", 
       "AAAAADBBBBB",
       "AAAABBBBBEF",
       "AAABBBBBDGH")) ## -->  "BBBBB"
LCSn(c("AAAXXBBB",
       "BBBXXDDD",
       "XXAAADDD")) ## --> fails due to greedy approach; should be "XX"

S5-RefClass to read MaxQuant .txt files

Description

This class is used to read MQ data tables using MQDataReader::readMQ() while holding the internal raw file –> short raw file name mapping (stored in a member called 'fn_map') and updating/using it every time MQDataReader::readMQ() is called.

Arguments

Details

Since MaxQuant changes capitalization and sometimes even column names, it seemed convenient to have a function which just reads a txt file and returns unified column names, irrespective of the MQ version. So, it unifies access to columns (e.g. by using lower case for ALL columns) and ensures columns are identically named across MQ versions:

 alternative term          new term
 -----------------------------------------
 protease                  enzyme
 protein.descriptions      fasta.headers
 potential.contaminant     contaminant
 mass.deviations           mass.deviations..da.
 basepeak.intensity        base.peak.intensity

We also correct 'reporter.intensity.*' naming issues to MQ 1.6 convention, when 'reporter.intensity.not.corrected' is present. MQ 1.5 uses: reporter.intensity.X and reporter.intensity.not.corrected.X MQ 1.6 uses: reporter.intensity.X and reporter.intensity.corrected.X

Note: you must find a regex which matches both versions, or explicitly add both terms if you are requesting only a subset of columns!

Fixes for msmsScans.txt: negative Scan Event Numbers in msmsScans.txt are reconstructed by using other columns

Automatically detects UTF8-BOM encoding and deals with it (since MQ2.4).

Example of usage:

  mq = MQDataReader$new()
  d_evd = mq$readMQ("evidence.txt", type="ev", filter="R", col_subset=c("proteins", "Retention.Length", "retention.time.calibration")) 

If the file is empty, this function shows a warning and returns NULL. If the file is present but cannot be read, the program will stop.

Wrapper to read a MQ txt file (e.g. proteinGroups.txt).

Value

A data.frame of the respective file

Replaces values in the mq.data member with (binary) values. Most MQ tables contain columns like 'contaminants' or 'reverse', whose values are either empty strings or "+", which is inconvenient and can be much better represented as TRUE/FALSE. The params valid_entries and replacements contain the matched pairs, which determine what is replaced with what.

Returns TRUE if successful.

Methods

getInvalidLines()

Detect broken lines (e.g. due to Excel import+export)

When editing a MQ txt file in Microsoft Excel, saving the file can cause it to be corrupted, since Excel has a single cell content limit of 32k characters (see http://office.microsoft.com/en-001/excel-help/excel-specifications-and-limits-HP010342495.aspx) while MQ can easily reach 60k (e.g. in oxidation sites column). Thus, affected cells will trigger a line break, effectively splitting one line into two (or more).

If the table has an 'id' column, we can simply check the numbers are consecutive. If no 'id' column is available, we detect line-breaks by counting the number of NA's per row and finding outliers. The line break then must be in this line (plus the preceeding or following one). Depending on where the break happened we can also detect both lines right away (if both have more NA's than expected).

Currently, we have no good strategy to fix the problem since columns are not aligned any longer, which leads to columns not having the class (e.g. numeric) they should have. (thus one would need to un-do the linebreak and read the whole file again)

[Solution to the problem: try LibreOffice 4.0.x or above – seems not to have this limitation]

@return Returns a vector of indices of broken (i.e. invalid) lines

Class to read an mzTab file and store the tables internally.

Description

The 'sections' field is initialized after $readMzTab was called. The 'fn_map' fields should be initialized via ...$fn_map$readMappingFile(...) manually if user-defined filename mappings are desired and is automatically updated/queried when $readMzTab is called.

Fields

sections

MzTab sections as list. Valid list entries are: "MTD", "PRT", "PEP", "PSM", "SML", "filename" and "comments"

fn_map

FilenameMapper which can translate raw filenames into something shorter

Methods

RTUnitCorrection(dt)

Convert all RT columns from seconds (OpenMS default) to minutes (MaxQuant default)

getEvidence()

Basically the PSM table and additionally columns named 'raw.file' and 'fc.raw.file'.

getMSMSScans(identified_only = FALSE)

Basically the PSM table (partially renamed columns) and additionally two columns 'raw.file' and 'fc.raw.file'. If identified_only is TRUE, only MS2 scans which were identified (i.e. a PSM) are returned – this is equivalent to msms.txt in MaxQuant.

getParameters()

Converts internal mzTab metadata section to a two column key-value data.frame similar to MaxQuants parameters.txt.

getProteins()

Basically the PRT table ...

getSummary()

Converts internal mzTab metadata section to a two data.frame with columns 'fc.raw.file', 'ms.ms.identified....' similar to MaxQuants summary.txt.

renameColumns(dt, namelist)

Renames all columns and throws a warning if a column does not exist in the data

Quality control for proteomics data

Description

Quality control for proteomics data

Define a Singleton class which holds the full raw filenames (+path) and their PSI-MS CV terms for usage in the mzQC metadata

Description

The internal data is filled using, e.g. 'getMetaFilenames()'

Super class

R6P::Singleton -> QCMetaFilenames

Public fields

Methods

Public methods

• QCMetaFilenames$clone()

Method clone()

The objects of this class are cloneable with this method.

Usage

QCMetaFilenames$clone(deep = FALSE)

Arguments

Relative standard deviation (RSD)

Description

Simply CV*100

Usage

RSD(x)

Arguments

Value

RSD

Return a tree plot with a possible alignment tree.

Description

This allows the user to judge which Raw files have similar corrected RT's (i.e. where aligned successfully). If there are clear sub-clusters, it might be worth introducing artifical fractions into MaxQuant, to avoid ID-transfer between these clusters (use the MBR-Align and MBR-ID-Transfer metrics to support the decision).

Usage

RTalignmentTree(df_evd, col_fraction = c())

Arguments

Details

If the input contains fractions, leaf nodes will be colored accordingly. Distinct sub-clusters should have their own color. If not, MaxQuant's fraction settings should be optimized. Note that introducing fractions in MaxQuant will naturally lead to a clustering here (it's somewhat circular).

Value

ggplot object containing the correlation tree

Compute the fraction of features per Raw file which have an acceptable RT difference after alignment

Description

Using the result from 'alignmentCheck()', score the features of every Raw file and see if they have been properly aligned. Returned value is between 0 (bad) and 1 (all aligned).

Usage

ScoreInAlignWindow(data, allowed.deltaRT = 1)

Arguments

Value

A data.frame with one row for each raw.file and columns 'raw.file' and 'withinRT' (0-1)

Query a YAML object for a certain parameter.

Description

If the object has the param, then return it. If the param is unknown, create it with the given default value and return the default.

Fields

yamlObj

A Yaml object as created by yaml.load

Methods

getYAML(param_name, default, min = NA, max = NA)

Query this YAML object for a certain parameter and return its value. If it does not exist it is created with a default value. An optional min/max range can be specified and will be enforced if the value is known (default will be used upon violation).

setYAML(param_name, value)

Set a YAML parameter to a certain value. Overwrites the old value or creates a new entry if hithero unknown.

writeYAML(filename)

Write YAML config (including some documentation) to a YAML file. Returns TRUE on success (always), unless writing the file generates an error.

Examples

    yc = YAMLClass$new(list())
    val = yc$getYAML("cat$subCat", "someDefault")
    val  ## someDefault
    val = yc$setYAML("cat$subCat", "someValue")
    val  ## someValue
    yc$getYAML("cat$subCat", "someDefault") ## still 'someValue' (since its set already)

Verify an alignment by checking the retention time differences of identical peptides across Raw files

Description

The input is a data frame containing feature evidence with corrected retention times, e.g. a 'calibrated.retention.time' column.

Usage

alignmentCheck(data, referenceFile)

Arguments

Details

Note that this function must be given real MS/MS identifications only (type "MULTI-MSMS") in order to work correctly!

For each peptide sequence (and charge) in the reference Raw file, this function looks up the already calibrated retention time difference of the same feature in all other files. For every comparison made, we report the RT difference. If alignment worked perfectly, the differences are very small (<1 min).

An 'id' column must be present, to enable mapping the result of this function to the original data frame.

A reference Raw file can be identified using 'findAlignReference()'. If Maxquants experimental design included pre-fractionation, a column named 'fraction' should be given and 'referenceFile' should be empty. This function will pick the one Raw file for each fraction (the first in order) to use as reference. Only the immediately neighbouring fractions will be matched to this reference.

Value

A data.frame containing the RT diff for each feature found in a Raw file and the reference.

Add the value of a variable to an environment (fast append)

Description

The environment must exist, and its name must be given as string literal in 'env_name'! The value of the variable 'v' will be stored under the name given in 'v_name'. If 'v_name' is not given, a variable name will be created by increasing an internal counter and using the its value padded with zeros as name (i.e., "0001", "0002" etc).

Usage

appendEnv(env_name, v, v_name = NULL)

Arguments

Value

Always TRUE

Collects all 'mzQC' members from each entry in lst_qcMetrics and stores them in an overall mzQC object, which can be written to disk (see writeMZQC()) or augmented otherwise

Description

Collects all 'mzQC' members from each entry in lst_qcMetrics and stores them in an overall mzQC object, which can be written to disk (see writeMZQC()) or augmented otherwise

Usage

assembleMZQC(lst_qcMetrics, raw_file_mapping)

Arguments

Value

An MzQCmzQC object (root object of an mzQC document)

Assign set numbers to a vector of values.

Description

Each set has size set_size (internally optimized using correctSetSize), holding values from 'values'. This gives n such sets and the return value is just the set index for each value.

Usage

assignBlocks(values, set_size = 5, sort_values = TRUE)

Arguments

Value

Vector (same length as input) with set numbers

Examples

 #library(PTXQC)
 assignBlocks(c(1:11, 1), set_size = 3, sort_values = FALSE)
 ## --> 1 1 1 2 2 2 3 3 3 4 4 1
 

Boxplots - one for each condition (=column) in a data frame.

Description

Given a data.frame with two/three columns in long format (name, value, [contaminant]; in that order), each group (given from 1st column) is plotted as a bar. Contaminants (if given) are separated and plotted as yellow bars.

Usage

boxplotCompare(
  data,
  log2 = TRUE,
  ylab = "intensity",
  mainlab = ylab,
  sublab = "",
  boxes_per_page = 30,
  abline = NA,
  coord_flip = TRUE,
  names = NA
)

Arguments

Details

Boxes are shaded: many NA or Inf lead to more transparency. Allows to easily spot sparse groups

Value

List of ggplot objects

Return color brew palettes, but fail hard if number of requested colors is larger than the palette is holding.

Description

Internally calls 'brewer.pal(n, palette)', checking 'n' beforehand.

Usage

brewer.pal.Safe(n = 3, palette = "Set1")

Arguments

Value

character vector of colors

Calls FUN on a subset of data in blocks of size 'subset_size' of unique indices.

Description

One subset consists of 'subset_size' unique groups and thus of all rows which in 'data' which have any of these groups. The last subset might have less groups, if the number of unique groups is not dividable by subset_size.

Usage

byX(data, indices, subset_size = 5, FUN, sort_indices = TRUE, ...)

Arguments

Details

FUN is applied on each subset.

Value

list of function result (one entry for each subset)

Examples

 byX(data.frame(d=1:10), 1:10, 2, sum)
 

Same as byX, but with more flexible group size, to avoid that the last group has only a few entries (<50% of desired size).

Description

The 'subset_size' param is internally optimized using correctSetSize and then byX is called.

Usage

byXflex(data, indices, subset_size = 5, FUN, sort_indices = TRUE, ...)

Arguments

Value

list of function result (one entry for each subset)

Examples

 stopifnot(
   byXflex(data.frame(d=1:10), 1:10, 2, sum, sort_indices = FALSE) ==
   c(3, 7, 11, 15, 19)
 )
 

When MaxQuant is run with a wrong locale (i.e. the decimal separator is not a '.', but a ','), then MaxQuant results are plainly wrong and broken. The can be detected by, e.g. checking for negative charge annotation

Description

When MaxQuant is run with a wrong locale (i.e. the decimal separator is not a '.', but a ','), then MaxQuant results are plainly wrong and broken. The can be detected by, e.g. checking for negative charge annotation

Usage

checkEnglishLocale(df_evd)

Arguments

Combine several data structs into a final picture for segmentation incurred by 'Match-between-runs'.

Description

qMBRSeg_Dist_inGroup might be empty if there are only singlets (transferred and genuine), but then the scores will be pretty boring as well (100

Usage

computeMatchRTFractions(qMBR, qMBRSeg_Dist_inGroup)

Arguments

Value

A data.frame which details the distribution of singlets and pairs (inRT and outRT) for each Raw file and genuine vs. all

Re-estimate a new set size to split a number of items into equally sized sets.

Description

This is useful for plotting large datasets where multiple pages are needed. E.g. you know that you need 101 barplots, but you only want to fit about 25 per page. Naively one would now do five plots, with the last one only containing a single barplot. Using this function with correctSetSize(101, 25) would tell you to use 26 barplots per page, so you end up with four plots, all roughly equally filled. It also works the other extreme case, where your initial size is chosen slightly too high, e.g. Sets of size 5 for just 8 items is too much, because we can reduce the set size to 4 and still need two sets but now they are much more equally filled (correctSetSize(8, 5) == 4).

Usage

correctSetSize(item_count, initial_set_size)

Arguments

Details

We allow for up to set sizes of 150% from default, to avoid the last set being sparse (we remove it and distribute to the other bins) 150 Once the number of sets is fixed, we distribute all items equally.

E.g. 6 items & initial_set_size=5, would result in 2 bins (5 items, 1 item), but we'd rather have one bin of 6 items or 8 items & initial_set_size=5, would result in 2 bins (5+3 items), since the last set is more than half full, but we'd rather have 4+4

Value

re-estimated set size which a set should hold in order to avoid underfilled sets

Examples

 stopifnot(
   correctSetSize(8, 5) == 4
 )
 stopifnot(
   correctSetSize(101, 25) == 26
 )
 

Create a quality control report (in PDF format).

Description

This is the main function of the package and the only thing you need to call directly if you are just interested in getting a QC report.

Usage

createReport(
  txt_folder = NULL,
  mztab_file = NULL,
  yaml_obj = list(),
  report_filenames = NULL,
  enable_log = FALSE
)

Arguments

Details

You need to provide either a) the folder name of the 'txt' output, as generated by MaxQuant or an mzTab file or b) an mzTab file as generated by the OpenMS QualityControl TOPP tool (other mzTab files will probably not work)

Optionally, provide a YAML configuration object, which allows to (de)activate certain plots and holds other parameters. The yaml_obj is complex and best obtained by running this function once using the default (empty list). A full YAML configuration object will be written in the 'txt' folder you provide and can be loaded using yaml.load.

The PDF and the config file will be stored in the given txt folder.

Value

List with named filename strings, e.g. $yaml_file, $report_file etc..

Note

You need write access to the txt/mzTab folder!

For updates, bug fixes and feedback please visit https://github.com/cbielow/PTXQC.

Creates a yaml file storing the parameters that are used for creating the PTXQC report and returns these parameters as well as a list of available qc-Metrics objects.

Description

Valid parameters are: param_useMQPAR, add_fs_col, id_rate_bad, id_rate_great , pg_ratioLabIncThresh , param_PG_intThresh, param_EV_protThresh , param_EV_intThresh, param_EV_pepThresh , yaml_contaminants, param_EV_MatchingTolerance, param_evd_mbr , param_EV_PrecursorTolPPM, param_EV_PrecursorOutOfCalSD , param_EV_PrecursorTolPPMmainSearch, param_MSMSScans_ionInjThresh, param_OutputFormats and param_PageNumbers

Please provide them as a list() of this format: list$parameter_name

Usage

createYaml(
  yc,
  param = list(),
  DEBUG_PTXQC = FALSE,
  txt_files = NULL,
  metrics = NULL
)

Arguments

Value

list of parameters used for creating the report and list of qc-Metrics objects

Make a color (given as name or in RGB) darker by factor x = [0 = black, 1=unchanged]

Description

Make a color (given as name or in RGB) darker by factor x = [0 = black, 1=unchanged]

Usage

darken(color, factor = 0.8)

Arguments

Value

darkened color

Replace 0 with NA in a vector

Description

Replace 0 with NA in a vector

Usage

del0(x)

Arguments

Value

Vector of same size as 'x', with 0's replaced by NA

Removes the longest common prefix (LCP) from a vector of strings.

Description

You should provide only unique strings (to increase speed). If only a single string is given, the empty string will be returned unless minOutputLength is set.

Usage

delLCP(x, min_out_length = 0, add_dots = FALSE)

Arguments

Value

Shortened vector of strings

Examples

  delLCP(c("TK12345_H1"), min_out_length=0)
  ## ""

  delLCP(c("TK12345_H1"), min_out_length=4)
  ## "5_H1"

  delLCP(c("TK12345_H1"), min_out_length=4, add_dots = TRUE)
  ## "..5_H1"

  delLCP(c("TK12345_H1", "TK12345_H2"), min_out_length=4)
  ## "5_H1" "5_H2"

  delLCP(c("TK12345_H1", "TK12345_H2"), min_out_length=4, add_dots = TRUE)
  ## "..5_H1" "..5_H2"

  delLCP(c("TK12345_H1", "TK12345_H2"), min_out_length=8)
  ## "12345_H1", "12345_H2"

  delLCP(c("TK12345_H1", "TK12345_H2"), min_out_length=8, add_dots = TRUE)
  ## "TK12345_H1", "TK12345_H2" (unchanged, since '..' would add another two)

  delLCP(c("TK12345_H1", "TK12345_H2"), min_out_length=60)
  ## "TK12345_H1", "TK12345_H2" (unchanged)

  delLCP(c("TK12345_H1", "TK12345_H2"), min_out_length=60, add_dots = TRUE)
  ## "TK12345_H1", "TK12345_H2" (unchanged)

Removes the longest common suffix (LCS) from a vector of strings.

Description

Removes the longest common suffix (LCS) from a vector of strings.

Usage

delLCS(x)

Arguments

Value

Shortened vector of strings

Examples

delLCS(c("TK12345_H1"))                     ## ""
delLCS(c("TK12345_H1", "TK12345_H2"))       ## "TK12345_H1" "TK12345_H2" 
delLCS(c("TK12345_H1", "TK12!45_H1"))       ## "TK123"    "TK12!" 
 

Return list of raw file names which were reported by MaxQuant as reference point for alignment.

Description

There is only one reference point which has '0' in 'retention.time.calibration' column in evidence.txt as corrected RT. This is true for most MaxQuant versions and also true for fractions. However, some evidence.txt files show 0.03 as an averaged minimum per Raw file. We use the raw.file with the smallest average as reference.

Usage

findAlignReference(data)

Arguments

Details

Note that MaxQuant uses a guide tree to align the Raw files, so the order of files does not influence the alignment. But the first file will always be used as reference point when reporting delta-RTs. And this file is also used by PTXQC as reference file vs all other files to find the real calibration function (see alignmentCheck()).

This function might return multiple raw file names (if MQ decides to change its mind at some point in the future). In this case the result should be treated with caution or (better) regarded as failure.

Value

List of reference raw files (usually just one)

Detect (and fix) MaxQuant mass recalibration columns, since they sometimes report wrong values.

Description

Returns a list of items for both diagnostics and possibly a fixed evidence data.frame. Also two strings with messages are returned, which can serve as user message for pre and post calibration status.

Usage

fixCalibration(
  df_evd,
  df_idrate = NULL,
  tolerance_sd_PCoutOfCal = 2,
  low_id_rate = 1
)

Arguments

Value

list of data (stats, affected_raw_files, df_evd, recal_message, recal_message_post)

Flatten lists of lists with irregular depths to just a list of items, i.e. a list of the leaves (if you consider the input as a tree).

Description

Flatten lists of lists with irregular depths to just a list of items, i.e. a list of the leaves (if you consider the input as a tree).

Usage

flattenList(x)

Arguments

Value

A flat list

Assign a relative abundance class to a set of (log10) abundance values

Description

Abundances (should be logged already) are grouped into different levels, starting from the smallest values ("low") to the highest values ("high"). Intermediate abundances are either assigned as "mid", or "low-mid". If the range is too large, only "low" and "high" are assigned, the intermediate values are just numbers.

Usage

getAbundanceClass(x)

Arguments

Details

Example: getAbundanceClass(c(12.4, 17.1, 14.9, 12.3)) ## –> factor(c("low", "high", "mid", "low"))

Value

Vector of factors corresponding to input with abundance class names (e.g. low, high)

Estimate the empirical density and return it

Description

Estimate the empirical density and return it

Usage

getECDF(samples, y_eval = (1:100)/100)

Arguments

Value

Data.frame with columns 'x', 'y'

Examples

  plot(getECDF(rnorm(1e4)))
  

Determine if a file is 'UTF-8' or 'UTF-8-BOM' (as of MQ2.4) or 'UTF-16BE' or 'UTF-16LE'

Description

Determine if a file is 'UTF-8' or 'UTF-8-BOM' (as of MQ2.4) or 'UTF-16BE' or 'UTF-16LE'

Usage

getFileEncoding(filename)

Arguments

Value

” if the file does not exist or is not readable

Extract fragment mass deviation errors from a data.frame from msms.txt

Description

Given a data.frame as obtainable from a msms.txt with - a 'mass.analyzer' column which contains only a single value for the whole column - a 'mass.deviations..da.' and (if available) 'mass.deviations..ppm.' - a 'masses' column (only required if 'mass.deviations..ppm.' is unavailable and the mass.analyzer indicates hig-res data)

Usage

getFragmentErrors(x, recurse = 0)

Arguments

Details

Mass deviations are extracted from the columns, e.g. each cell containing values separated by semicolons is split into single values. The appropriate unit is chosen (Da or ppm, depending on ITMS or FTMS data). Also the fragmentation type can be used: CID indicates ITMS, HCD to FTMS. This is not 100

Sometimes, peptides are identified purely based on MS1, i.e. have no fragments. These will be ignored.

If ppm mass deviations are not available, errors in Da will be converted to ppm using the corresponding mass values.

Value

Data frame with mass errors ('msErr') and their 'unit' (Da or ppm) or NULL (if no fragments were given)

Create an HTML table with an extra header row

Description

Create an HTML table with an extra header row

Usage

getHTMLTable(data, caption = NA)

Arguments

Value

table as html character string for cat()'ing into an html document

Examples

  data = data.frame(raw.file = letters[1:4],
                    id.rate = 3:6)
  getHTMLTable(data, 
               caption = "some header line")

Retrieve a parameter value from a mqpar.xml file

Description

If the file has the param, then return it as string. If the file is missing, warning is shown and NULL is returned. If the param (i.e. XML tag) is unknown or cannot be extracted, the program will quit (since this is a hard error). When multiple occurrences of the param are found (usually due to parameter groups), we test if the values are all identical. If so, the value is returned. If the values are different, a warning is emitted and NULL is returned unless 'allow_multiple = TRUE'

Usage

getMQPARValue(mqpar_filename, xpath, allow_multiple = FALSE)

Arguments

Details

E.g. calling getMQPARValue("mqpar.xml", "//firstSearchTol") will look up the line <firstSearchTol>20</firstSearchTol> and return "20" (string!).

Value

The stored value as string(!)

Find the local maxima in a vector of numbers.

Description

A vector of booleans is returned with the same length as input which contains TRUE when there is a maximum. Simply sum up the vector to get the number of maxima.

Usage

getMaxima(x, thresh_rel = 0.2)

Arguments

Value

Vector of bool's, where TRUE indicates a local maximum.

Examples

    r = getMaxima(c(1,0,3,4,5,0))                                
    all(r == c(TRUE,FALSE,FALSE,FALSE,TRUE,FALSE))
    
    getMaxima(c(1, NA, 3, 2, 3, NA, 4, 2, 5))
    

Extract meta information (orderNr, metric name, category) from a list of Qc metric objects

Description

Extract meta information (orderNr, metric name, category) from a list of Qc metric objects

Usage

getMetaData(lst_qcMetrics)

Arguments

Value

data.frame with columns 'name', 'order' and 'cat' (category)

Parses the given mqpar.xml file (or, if not found, tries the 'txt_folder' + '/../../' folder (i.e. where the raw data should be)) to extract the full filepaths for all Raw files

Description

Parses the given mqpar.xml file (or, if not found, tries the 'txt_folder' + '/../../' folder (i.e. where the raw data should be)) to extract the full filepaths for all Raw files

Usage

getMetaFilenames(mqpar_file, txt_folder)

Arguments

Value

May return 'NULL' if no mqpar.xml could be found. Otherwise: data.frame with columns:

• 'file' (no path), 'path' (full path incl. names)

• 'file_no_suffix' (as 'file' but without suffix)

• 'CV' (CV term for filetype, e.g. for Thermo Raw)

Get all currently available metrics

Description

Get all currently available metrics

Usage

getMetricsObjects(DEBUG_PTXQC = FALSE)

Arguments

Value

List of matric objects

Create a principal component analysis (PCA) plot for the first two dimensions.

Description

Create a principal component analysis (PCA) plot for the first two dimensions.

Usage

getPCA(data, do_plot = TRUE, connect_line_order = NA, gg_layer)

Arguments

Value

[invisible] Named list with "PCA": The PCA object as returned by prcomp, access $x for PC values and "plots": list of plot objects (one or two)

Examples

n = 5
m = 10
data = matrix(runif(n * m), nrow = n, ncol = m)
rownames(data) = 1:n
getPCA(data, connect_line_order = 1:n, gg_layer = ggplot2::ggtitle("test"))

Extract the number of peptides observed per Raw file from an evidence table.

Description

Required columns are "fc.raw.file", "modified.sequence" and "is.transferred".

Usage

getPeptideCounts(df_evd)

Arguments

Details

If match-between-runs was enabled during the MaxQuant run, the data.frame returned will contain separate values for 'transferred' evidence plus an 'MBRgain' column, which will give the extra MBR evidence in percent.

Value

Data.frame with columns 'fc.raw.file', 'counts', 'category', 'MBRgain'

Extract the number of protein groups observed per Raw file from an evidence table.

Description

Required columns are "protein.group.ids", "fc.raw.file" and "is.transferred".

Usage

getProteinCounts(df_evd)

Arguments

Details

If match-between-runs was enabled during the MaxQuant run, the data.frame returned will contain separate values for 'transferred' evidence plus an 'MBRgain' column, which will give the extra MBR evidence in percent.

Value

Data.frame with columns 'fc.raw.file', 'counts', 'category', 'MBRgain'

Generate a Heatmap from a list of QC measurements.

Description

Each list entry is a data.frame with two columns. The first one contains the Raw file name (or the short version). and should be named 'raw.file' (or 'fc.raw.file'). The second column's name must be an expression (see ?plotmath) and contains quality values in the range [0,1]. If values are outside this range, a warning is issued and values are cut to the nearest allowed value (e.g. '1.2' becomes '1'). List entries are merged and columns are ordered by name.

All substrings enclosed by 'X[0-9]*X.' will be removed (can be used for sorting columns). The resulting string is evaluated as an expression. E.g. parse(text = <colname>)

Usage

getQCHeatMap(lst_qcMetrics, raw_file_mapping)

Arguments

Details

To judge the overall quality of each raw file a summary column is added, values being the mean of all other columns per row.

Value

A ggplot object for printing

Assembles a list of output file names, which will be created during reporting.

Description

You can combine **report_name_has_folder** (and **mzTab_filename** for mzTab files) to obtain report filenames which are even more robust to moving around (since they contain infixes of the mzTab filename and the folder), e.g. '@em 'report_HEK293-study_myProjects.html“, where the input was ‘mzTab_filename=’HEK293-study.mzTab‘ and 'folder=’c:/somePath/myProjects/'.

Usage

getReportFilenames(
  folder,
  report_name_has_folder = TRUE,
  mzTab_filename = NULL
)

Arguments

Value

List of output file names (just names, no file is created) with list entries: **yaml_file**, **heatmap_values_file**, **R_plots_file**, **filename_sorting**, **mzQC_file**, **log_file**, **report_file_prefix**, **report_file_PDF**, **report_file_HTML**

Get an mzQC runQuality without actual metrics, but with full metadata

Description

Get an mzQC runQuality without actual metrics, but with full metadata

Usage

getRunQualityTemplate(fc.raw.file, raw_file_mapping)

Arguments

Value

An MzQCrunQuality object

Function to thin out the number of labels shown on an axis in GGplot

Description

By default, 20 labels (or up to 40 see below) are shown. If the number of items is less than twice the number of desired labels, all labels will be shown (to avoid irregular holes for some labels). I.e. if n=20, and x has 22 entries, there would be only two labels removed, giving a very irregular picture. It only becomes somewhat regular if after any label there is at least one blank, i.e. at most half the entries are labeled. #' Example: ## p is any ggplot object p + scale_y_discrete(breaks = ggAxisLabels) ## customize 'n' my.ggAxisLabels = function(x) ggAxisLabels(x, n = 4) p + scale_y_discrete(breaks = my.ggAxisLabels)

Usage

ggAxisLabels(x, n = 20)

Arguments

Value

Shortened version of 'x'

Plot a text as graphic using ggplot2.

Description

Plot a text as graphic using ggplot2.

Usage

ggText(title, text, col = "black")

Arguments

Value

ggplot object

Grep with values returned instead of indices.

Description

The parameter 'value' should not be passed to this function since it is passed internally already.

Usage

grepv(reg, data, ...)

Arguments

Value

values of data which matched the regex

Examples

  grepv("x", c("abc", "xyz"))
  ## --> "xyz"
  

Check how close transferred ID's after alignment are to their genuine IDs within one Raw file.

Description

The input is a data.frame containing feature evidence with corrected retention times, e.g. a 'calibrated.retention.time' column.

Usage

idTransferCheck(df_evd_all)

Arguments

Details

Note that this function must be given MS/MS identifications of type "MULTI-MSMS" and "MSMS-MATCH". It will stop() otherwise.

We compare for each peptide sequence (and charge) the RT difference within groups of either genuine as well as mixed pairs. For every comparison made, we report the RT span If alignment worked perfectly, the span are very small (<1 min), for the mixed group, i.e. the pairs are accidentally split 3D peaks. Alignment performance has no influence on the genuine-only groups.

Note: We found early MaxQuant versions (e.g. 1.2.2.5) to have an empty 'modified.sequence' column for 'MULTI-MATCH' entries. The sequence which SHOULD be present is equal to the immediate upper row. This is what we use to guess the sequence. However, this relies on the data.frame not being subsetted before (we can sort using the 'id' column)!

Value

A data.frame containing the RT diff for each ID-group found in a Raw file (bg = genuine).

For grouped peaks: separate them into in-width vs. out-width class.

Description

Looking at groups only: Compute the fraction of 3D-peak pair groups per Raw file which have an acceptable RT difference after alignment using the result from 'idTransferCheck()', i.e. compute the fraction of groups which are within a certain RT tolerance.

Usage

inMatchWindow(data, df.allowed.deltaRT)

Arguments

Details

Returned value is between 0 (bad) and 1 (all within tolerance).

Value

A data.frame with one row for each raw.file and columns 'raw.file' and score 'withinRT' (0-1)

Count the number of chars of the longest common prefix

Description

Count the number of chars of the longest common prefix

Usage

lcpCount(x)

Arguments

Value

Length of LCP

Count the number of chars of the longest common suffix

Description

Count the number of chars of the longest common suffix

Usage

lcsCount(x)

Arguments

Value

Length of LCS

Get the longest common prefix from a set of strings.

Description

Input is converted to character (e.g. from factor) first.

Usage

longestCommonPrefix(strings)

Arguments

Value

Single string - might be empty ("")

Examples

  longestCommonPrefix(c("CBA.321", "CBA.77654", ""))    ## ""
  longestCommonPrefix(c("CBA.321", "CBA.77654", "CB"))  ## "CB"
  longestCommonPrefix(c("ABC.123", "ABC.456"))          ## "ABC."
  longestCommonPrefix(c("nothing", "in", "common"))     ## ""

Like longestCommonPrefix(), but on the suffix.

Description

Like longestCommonPrefix(), but on the suffix.

Usage

longestCommonSuffix(strings)

Arguments

Value

Single string - might be empty ("")

Examples

 longestCommonSuffix(c("123.ABC", "45677.ABC", "BC"))  ## "BC"
 longestCommonSuffix(c("123.ABC", "", "BC"))           ## ""
 longestCommonSuffix(c("123.ABC", "45677.ABC"))        ## ".ABC"
 longestCommonSuffix(c("nothing", "in", "common"))     ## ""
 

Convert list of (mixed)modifications to a frequency table

Description

Convert list of (mixed)modifications to a frequency table

Usage

modsToTable(mod_list)

Arguments

Value

A data.frame with 'modification_names' and 'Freq' (0-100)

Examples

modsToTable(c("Ox (M)", 
              "Unmodified",
              "Ox (M),Acetyl (Prot N-term)",
              "2 Ox (M)",
              "Unmodified",
              "Unmodified"))

Convert list of (mixed)modifications to a frequency table

Description

Convert list of (mixed)modifications to a frequency table

Usage

modsToTableByRaw(
  df_evd,
  name_unmod = "Unmodified",
  name_unmod_inverse = "Modified (total)"
)

Arguments

Value

A data.table with 'fc.raw.file', 'modification_names' (factor), and 'Freq' (0-100)

Examples

data = data.frame(fc.raw.file = rep(c("file A", "file B"),
                                each = 3),
       modifications = c("Oxidation (M)",
                        "Unmodified",
                        "Oxidation (M),Acetyl (Protein N-term)",
                        "2 Oxidation (M)", 
                        "Unmodified", "Unmodified"))
modsToTableByRaw(data)

Prepare a Mosaic plot of two columns in long format.

Description

Found at http://stackoverflow.com/questions/19233365/how-to-create-a-marimekko-mosaic-plot-in-ggplot2 Modified (e.g. to pass R check)

Usage

mosaicize(data)

Arguments

Details

Returns a data frame, which can be used for plotting and has the following columns: 'Var1' - marginalized values from 1st input column 'Var2' - marginalized values from 2nd input column 'Freq' - relative frequency of the combination given in [Var1, Var2] 'margin_var1' - frequency of the value given in Var1 'var2_height' - frequency of the value given in Var2, relative to Var1 'var1_center' - X-position when plotting (large sets get a larger share)

Value

Data.frame

Examples

  data = data.frame(raw.file = c(rep('file A', 100), rep('file B', 40)),
                    charge = c(rep(2, 60), rep(3, 30), rep(4, 10),
                               rep(2, 30), rep(3, 7), rep(4, 3)))
  mosaicize(data)                             
  

paste with newline as separator

Description

paste with newline as separator

Usage

pasten(...)

Arguments

Value

return value of paste()

Examples

  pasten("newline","separated")
  ## --> "newline\nseparated"
  

paste with tab as separator

Description

paste with tab as separator

Usage

pastet(...)

Arguments

Value

return value of paste()

Examples

  pastet("tab","separated")
  ## --> "tab\tseparated"
  

Determine fraction of evidence which causes segmentation, i.e. sibling peaks at different RTs confirmed either by genuine or transferred MS/MS.

Description

Sometimes, MQ splits a feature into 2 or more if the chromatograpic conditions are not optimal and there is a drop in RT intensity. If both features contain successful MS/MS scans, we will find the same peptide twice (with slightly different RT) in the same charge state. This constitutes a natively split peak and is rare (95

Usage

peakSegmentation(df_evd_all)

Arguments

Details

If Match-between-runs is used and the RT alignment is not perfect, then a peptide might be inferred at a wrong RT position, even though this Raw file already contains MS/MS evidence of this peptide. Usually the number of peak duplicates rises drastically (e.g. only 75 In most cases, the RT is too far off to be a split peak. It's rather a lucky hit with accidentally the same mass-to-charge, and thus the intensity is random. To find by how much these peak pairs differ in RT, use idTransferCheck() and inMatchWindow().

Required columns are 'is.transferred', 'fc.raw.file', 'modified.sequence', 'charge', 'type'.

Note that this function must be given MS/MS identifications of type "MULTI-MSMS" and "MSMS-MATCH". It will stop() otherwise.

Value

A data.frame with one row per Raw file and three columns: 1) 2) 3)

Discretize RT peak widths by averaging values per time bin.

Description

Should be applied for each Raw file individually.

Usage

peakWidthOverTime(data, RT_bin_width = 2)

Arguments

Details

Returns a data.frame, where 'bin' gives the index of each bin, 'RT' is the middle of each bin and 'peakWidth' is the averaged peak width per bin.

Value

Data.frame with columns 'bin', 'RT', 'peakWidth'

Examples

  data = data.frame(retention.time = seq(30,200, by=0.001)) ## one MS/MS per 0.1 sec
  data$retention.length = seq(0.3, 0.6, length.out = nrow(data)) + rnorm(nrow(data), 0, 0.1)
  d = peakWidthOverTime(data)
  plot(d$RT, d$peakWidth)
  
  

Plot a table with row names and title

Description

Restriction: currently, the footer will be cropped at the table width.

Usage

plotTable(
  data,
  title = "",
  footer = "",
  col_names = colnames(data),
  fill = c("grey90", "grey70"),
  col = "black",
  just = "centre"
)

Arguments

Value

gTree object with class 'PTXQC_table'

Examples

  data = data.frame(raw.file = letters[1:4],
                    id.rate = 3:6)
  plotTable(data, 
            title = "Bad files",
            footer = "bottom", 
            col_names = c("first col", "second col"),
            col=c("red", "green"))

Colored table plot.

Description

Code taken from http://stackoverflow.com/questions/23819209/change-text-color-for-cells-using-tablegrob-in-r

Usage

plotTableRaw(data, colours = "black", fill = NA, just = "centre")

Arguments

Value

gTable

Plot bargraph of uncalibrated mass errors for each Raw file.

Description

Boxes are optionally colored to indicate that a MQ bug was detected or if PTXQC detected a too narrow search window.

Usage

plot_CalibratedMSErr(
  data,
  MQBug_raw_files,
  stats,
  y_lim,
  extra_limit = NA,
  title_sub = ""
)

Arguments

Value

GGplot object

Examples

  n = c(150, 1000, 1000, 1000)
  data = data.frame(fc.raw.file = repEach(letters[4:1], n),
                    mass.error..ppm. = c(rnorm(n[1], 1, 2.4),
                                         rnorm(n[2], 0.5, 0.5),
                                         rnorm(n[3], 0.1, 0.7),
                                         rnorm(n[4], 0.3, 0.8)))
  stats = data.frame(fc.raw.file = letters[4:1],
                     sd = c(2.4, 0.5, 0.7, 0.8),
                     outOfCal = c(TRUE, FALSE, FALSE, FALSE))           
  plot_CalibratedMSErr(data, MQBug_raw_files = letters[1], stats, y_lim = c(-20,20), 15, "subtitle")

The plots shows the charge distribution per Raw file. The output of 'mosaicize()' can be used directly.

Description

The input is a data.frame with columns 'Var1' - name of the Raw file 'Var2' - charge (used as fill color) 'Var1_center' - contains X-position of the Raw file 'Var2_height' - relative frequency of the charge 'Margin_var1' - where each row represents one peptide sequence.

Usage

plot_Charge(d_charge)

Arguments

Value

GGplot object

Examples

 data = data.frame(raw.file = c(rep('file A', 100), rep('file B', 40)),
                       data = c(rep(2, 60), rep(3, 30), rep(4, 10),
                                rep(2, 30), rep(3, 7), rep(4, 3)))
 plot_Charge(mosaicize(data))

Plot contaminants from evidence.txt, broken down into top5-proteins.

Description

Plot contaminants from evidence.txt, broken down into top5-proteins.

Usage

plot_ContEVD(data, top5)

Arguments

Value

GGplot object

Examples

 data = data.frame(intensity = 1:12, 
                   pname = rep(letters[1:3], 4), 
                   fc.raw.file = rep(paste("f", 1:4), each=3),
                   contaminant = TRUE)
 ## providing more proteins than present... d,e will be ignored
 plot_ContEVD(data, top5 = letters[1:5])
 ## classify 'c' as 'other'
 plot_ContEVD(data, top5 = letters[1:2])

Plot user-defined contaminants from evidence.txt

Description

Kolmogorov-Smirnoff p-values are plotted on top of each group. High p-values indicate that Andromeda scores for contaminant peptides are equal or higher compared to sample peptide scores, i.e. the probability that sample peptides scores are NOT greater than contaminant peptide scores.

Usage

plot_ContUser(data, name_contaminant, extra_limit, subtitle = NULL)

Arguments

Value

GGplot object

Examples

 data = data.frame(fc.raw.file = letters[1:3], 
                   variable = c(rep("spectralCount", 3),
                                 rep("intensity", 3),
                                 rep("above.thresh", 3),
                                 rep("score_KS", 3)),
                   value = c(10, 20, 15, 9, 21, 14, 0, 1, 1, 0.3, 0.01, 0.04))
 plot_ContUser(data, "myco", 5, "subtitle")

Plot Andromeda score distribution of contaminant peptide vs. matrix peptides.

Description

The data is expected to be an ECDF already, x being the Andromeda score, y being the culmulative probability. The Score is the probability of a Kolm.-Smirnoff test that the contaminant scores are larger (i.e. large p-values indicate true contamination). You will only see this plot if the but high-scoring contaminant peptides, which would erroneously give you a large p-value and make you believe your sample is contaminated although that's not the case.

Usage

plot_ContUserScore(data, raw.file, score)

Arguments

Value

GGplot object

Examples

 data = data.frame(x = 10:60,
                   y = c(seq(0,1,length=51), seq(0.1, 1, length=51)), 
                   condition = rep(c("sample","contaminant"), each=51))
 plot_ContUserScore(data, 'test file', 0.96)

Plot contaminants from proteinGroups.txt

Description

Plot contaminants from proteinGroups.txt

Usage

plot_ContsPG(data)

Arguments

Value

GGplot object

Examples

 data = data.frame( 'group' = letters[1:10], 'cont_pc' = 2:11, 'logAbdClass' = c("low","high"))
 plot_ContsPG(data)

Plot Protein groups per Raw file

Description

The input is a data.frame with protein/peptide counts, where 'category' designates the origin of information (genuine ID, transferred ID, or both).

Usage

plot_CountData(data, y_max, thresh_line, title)

Arguments

Value

GGplot object

Examples

 data = data.frame(fc.raw.file = rep(c("file A", "file B"), each=3),
                   counts = c(3674, 593, 1120, 2300, 400, 600), 
                   category = c("genuine","genuine+transferred","transferred"))
 plot_CountData(data, 6000, 4000, c("EVD: Protein Groups count", "gain: 23%"))

Plot some count data over time for each Raw file.

Description

The input is a data.frame with columns 'RT' - RT in seconds, representing one bin 'counts' - number of counts at this bin 'fc.raw.file' - name of the Raw file where each row represents one bin in RT.

Usage

plot_DataOverRT(
  data,
  title,
  y_lab,
  x_lim = range(data$RT),
  y_max = max(data$counts)
)

Arguments

Details

At most nine(!) Raw files can be plotted. If more are given, an error is thrown.

Value

GGplot object

Examples

 data = data.frame(fc.raw.file = rep(paste('file', letters[1:3]), each=30),
                            RT = seq(20, 120, length.out = 30),
                        counts = c(rnorm(30, 400, 20), rnorm(30, 250, 15), rnorm(30, 50, 15)))
 plot_DataOverRT(data, "some title", "count data")

Plot percent of identified MS/MS for each Raw file.

Description

Useful for a first overall impression of the data.

Usage

plot_IDRate(data, id_rate_bad, id_rate_great, label_ID)

Arguments

Details

The input is a data.frame with columns 'fc.raw.file' - name of the Raw file 'ms.ms.identified....' - fraction of identified MS/MS spectra in percent 'cat' - identification category as arbitrary string where each row represents one Raw file.

Value

GGplot object

Examples

 id_rate_bad = 20; id_rate_great = 35;
 label_ID = c("bad (<20%)" = "red", "ok (...)" = "blue", "great (>35%)" = "green")
 data = data.frame(fc.raw.file = paste('file', letters[1:3]),
                   ms.ms.identified.... = rnorm(3, 25, 15))
 data$cat = factor(cut(data$ms.ms.identified....,
                       breaks=c(-1, id_rate_bad, id_rate_great, 100),
                       labels=names(label_ID)))                  
 plot_IDRate(data, id_rate_bad, id_rate_great, label_ID)

Plot IDs over time for each Raw file.

Description

Uses plot_DataOverRT() internally.

Usage

plot_IDsOverRT(data, x_lim = range(data$RT), y_max = max(data$counts))

Arguments

Value

GGplot object

Examples

 data = data.frame(fc.raw.file = rep(paste('file', letters[1:3]), each=30),
                            RT = seq(20, 120, length.out = 30),
                        counts = c(rnorm(30, 400, 20), rnorm(30, 250, 15), rnorm(30, 50, 15)))
 plot_IDsOverRT(data)

Plot line graph of TopN over Retention time.

Description

Number of Raw files must be 6 at most. Function will stop otherwise.

Usage

plot_IonInjectionTimeOverRT(data, stats, extra_limit)

Arguments

Value

GGplot object

Examples

  data = data.frame(fc.raw.file = rep(c("d","a","x"), each=100),
                    rRT = seq(20, 120, length.out = 100),
                    medIIT = c(round(runif(100, min=3, max=5)),
                               round(runif(100, min=5, max=8)),
                               round(runif(100, min=1, max=3)))
                    )
  stats = data.frame(fc.raw.file = c("d","a","x"),
                     mean = c(4, 6.5, 2))
  plot_IonInjectionTimeOverRT(data, stats, 10)

Plot MaxQuant Match-between-runs alignment performance.

Description

The plots shows the correction function applied by MaxQuant, and the residual RT (ideally 0) of each peptide to its reference. Uncalibrated peptides are shown in red, calibrated ones in green. The MaxQuant RT correction which was applied prior is shown in blue. The range of this function can give hints if the allowed RT search window (20min by default) is sufficient or if MaxQuant should be re-run with more tolerant settings.

Usage

plot_MBRAlign(data, y_lim, title_sub, match_tol)

Arguments

Details

The input is a data.frame with columns 'calibrated.retention.time' - resulting (hopefully) calibratated RT after MQ-recal (the X-axis of the plot) 'retention.time.calibration' - delta applied by MaxQuant 'rtdiff' - remaining RT diff to reference peptide of the same sequence 'RTdiff_in' - is the feature aligned (within 'match_tol')? 'fc.raw.file_ext' - raw file where each row represents one peptide whose RT was corrected by MaxQuant.

Value

GGplot object

Examples

 data = data.frame(fc.raw.file_ext = "file A", ## more than one would be possible
                   calibrated.retention.time = c(20:100), 
                   retention.time.calibration = 6 + sin((20:100)/10))
 data$rtdiff = rnorm(nrow(data))
 data$RTdiff_in = c("green", "red")[1 + (abs(data$rtdiff) > 0.7)]
 
 plot_MBRAlign(data, c(-10, 10), "fancy subtitle", 0.7)

Plot MaxQuant Match-between-runs id transfer performance.

Description

The plots shows the different categories of peak classes

Usage

plot_MBRIDtransfer(data)

Arguments

Details

The input is a data.frame with columns 'fc.raw.file' - raw file name 'single' - fraction of peptides with are represent only once 'multi.inRT' - fraction of peptides with are represent multiple times, but within a certain RT peak width 'multi.outRT' - fraction of peptides with are represent multiple times, with large RT distance 'sample' - raw file where each row represents one peptide sequence.

Value

GGplot object

Examples

 data = data.frame(fc.raw.file = rep(c("file A", "file B"), each = 3),
                   single = c(0.9853628, 0.8323160, 0.9438375,
                              0.9825538, 0.8003763, 0.9329961), 
                   multi.inRT = c(0.002927445, 0.055101018, 0.017593087,
                                  0.005636457, 0.099640044, 0.031870056),
                   multi.outRT = c(0.01170978, 0.11258294, 0.03856946,
                                   0.01180972, 0.09998363, 0.03513386),
                   sample = rep(c("genuine", "transferred", "all"), 2))
 plot_MBRIDtransfer(data)

Plot MaxQuant Match-between-runs id transfer performance as a scatterplot.

Description

Per Raw file, the absolute number of transferred IDs as well as the relative gain in percent.

Usage

plot_MBRgain(data, title_sub = "")

Arguments

Details

The input is a data.frame with columns 'fc.raw.file' - raw file name 'abs' - absolute number of transferred ID's 'pc' - gain on top of genuine IDs [ where each row represents one rawfile.

Value

GGplot object

Examples

 data = data.frame(fc.raw.file = paste("file", letters[1:4]),
                   abs = c(5461, 5312, 3618, 502), 
                   pc = c(34, 32, 22, 2))
 plot_MBRgain(data, "MBR gain: 18%")

Plot bargraph of oversampled 3D-peaks.

Description

Per Raw file, at most three n's must be given, i.e. the fraction of 3D-peaks for n=1, n=2 and n=3(or more). The fractions must sum to 1 (=100

Usage

plot_MS2Decal(data)

Arguments

Value

GGplot object

Examples

  n = c(100, 130, 50)
  data = data.frame(file = repEach(paste(letters[1:3],"\nLTQ [Da]"), n),
                    msErr = c(rnorm(n[1], 0.5), rnorm(n[2], 0.0), rnorm(n[3], -0.5)),
                    type = c("forward", "decoy")[1+(runif(sum(n))>0.95)])
  plot_MS2Decal(data)

Plot bargraph of oversampled 3D-peaks.

Description

Per Raw file, at most three n's must be given, i.e. the fraction of 3D-peaks for n=1, n=2 and n=3(or more). The fractions must sum to 1 (=100

Usage

plot_MS2Oversampling(data)

Arguments

Value

GGplot object

Examples

  data = data.frame(fc.raw.file = rep(letters[1:3], each=3),
                    n = 1:3,
                    fraction = c(0.8, 0.1, 0.1, 0.6, 0.3, 0.1, 0.7, 0.25, 0.05))
  plot_MS2Oversampling(data)

Plot bargraph of missed cleavages.

Description

Per Raw file, an arbitrary number of missed cleavage classes (one per column) can be given. The total fraction of 3D-peaks must sum to 1 (=100 Columns are ordered by name.

Usage

plot_MissedCleavages(data, title_sub = "")

Arguments

Details

A visual threshold line is drawn at 75

Value

GGplot object

Examples

  data = data.frame(fc.raw.file = letters[1:5],
                    MC0 = c(0.8, 0.5, 0.85, 0.2, 0.9),
                    MC1 = c(0.1, 0.4, 0.05, 0.7, 0.0),
                    "MS2+" =  c(0.1, 0.1, 0.1, 0.1, 0.1),
                    check.names = FALSE)
  plot_MissedCleavages(data, "contaminant inclusion unknown")

Plot RT peak width over time

Description

The input is a data.frame with already averaged counts over binned RT-slices.

Usage

plot_RTPeakWidth(data, x_lim, y_lim)

Arguments

Value

GGplot object

Examples

 data = data.frame(fc.raw.file = rep(c("file A", "file B", "file C"), each=81),
                   RT = c(20:100), 
                   peakWidth = c(rnorm(81, mean=20), rnorm(81, mean=10), rnorm(81, mean=30)))
 plot_RTPeakWidth(data, c(10, 100), c(0, 40))

Plot ratios of labeled data (e.g. SILAC) from proteinGroups.txt

Description

The 'x' values are expected to be log2() transformed already.

Usage

plot_RatiosPG(df_ratios, d_range, main_title, main_col, legend_title)

Arguments

Value

GGplot object

Examples

 x1 = seq(-3, 3, by = 0.1)
 y1 = dnorm(x1)
 x2 = seq(-5, 1, by = 0.1)
 y2 = dnorm(x2, mean = -1)
 data = data.frame( x = c(x1,x2),
                    y = c(y1,y2), 
                    col = c(rep("ok", length(x1)), rep("shifted", length(x2))), 
                    ltype = c(rep("solid", length(x1)), rep("dotted", length(x2))))
 plot_RatiosPG(data, range(data$x), "Ratio plot", "red", "group")

Plot line graph of TopN over Retention time.

Description

Number of Raw files must be 6 at most. Function will stop otherwise.

Usage

plot_ScanIDRate(data)

Arguments

Value

GGplot object

Examples

  data = data.frame(fc.raw.file = factor(rep(c("d","a","x"), each=10), levels = c("d","a","x")),
                    scan.event.number = 1:10,
                    ratio = seq(40, 20, length.out=10),
                    count = seq(400, 200, length.out=10))
  plot_ScanIDRate(data)

Plot Total Ion Count over time

Description

The input is a data.frame with already averaged counts over binned RT-slices.

Usage

plot_TIC(data, x_lim, y_lim)

Arguments

Value

GGplot object

Examples

 data = data.frame(fc.raw.file = rep(c("file A", "file B", "file C"), each=81),
                   RT = c(20:100), 
                   intensity = c(rnorm(81, mean=20), rnorm(81, mean=10), rnorm(81, mean=30)))
 plot_TIC(data, c(10, 100), c(0, 40))

Plot line graph of TopN over Retention time.

Description

Number of Raw files must be 6 at most. Function will stop otherwise.

Usage

plot_TopN(data)

Arguments

Value

GGplot object

Examples

  data = data.frame(fc.raw.file = rep(c("d","a","x"), each=10),
                    scan.event.number = 1:10,
                    n = 11:20)
  plot_TopN(data)

Plot line graph of TopN over Retention time.

Description

Number of Raw files must be 6 at most. Function will stop otherwise.

Usage

plot_TopNoverRT(data)

Arguments

Value

GGplot object

Examples

  data = data.frame(fc.raw.file = rep(letters[1:3], each=100),
                    rRT = seq(20, 120, length.out = 100),
                    topN = c(round(runif(100, min=3, max=5)),
                             round(runif(100, min=5, max=8)),
                             round(runif(100, min=1, max=3)))
                    )
  plot_TopNoverRT(data)

A boxplot of uncalibrated mass errors for each Raw file.

Description

Boxes are optionally colored to indicate that a MQ bug was detected or if PTXQC detected a too narrow search window.

Usage

plot_UncalibratedMSErr(
  data,
  MQBug_raw_files,
  stats,
  y_lim,
  extra_limit,
  title_sub
)

Arguments

Value

GGplot object

Examples

  n = c(150, 1000, 1000, 1000)
  data = data.frame(fc.raw.file = repEach(letters[4:1], n),
                    uncalibrated.mass.error..ppm. = c(rnorm(n[1], 13, 2.4),
                                                      rnorm(n[2], 1, 0.5),
                                                      rnorm(n[3], 3, 0.7),
                                                      rnorm(n[4], 4.5, 0.8)))
  stats = data.frame(fc.raw.file = letters[4:1],
                     sd_uncal = c(2.4, 0.5, 0.7, 0.8),
                     outOfCal = c(TRUE, FALSE, FALSE, FALSE))           
  plot_UncalibratedMSErr(data, MQBug_raw_files = letters[1],
                         stats, y_lim = c(-20,20), 15, "subtitle")

Plot peptide modification frequencies

Description

The input is a data.frame, as obtained from modsToTableByRaw().

Usage

plot_peptideMods(tbl, y_max = NA, show_missing_modification_levels = TRUE)

Arguments

Value

GGplot object

Examples

 data = data.frame(fc.raw.file = rep(c("file A", "file B"), each=3),
                   modifications = c("Oxidation (M)",
                   "Unmodified",
                   "Oxidation (M), Acetyl (Protein N-term)",
                   "2 Oxidation (M)", 
                   "Unmodified", 
                   "Unmodified"))
 tbl = modsToTableByRaw(data)
 plot_peptideMods(tbl,show_missing_modification_levels = TRUE)

Distribute a set of points with fixed y-values on a stretch of the x-axis.

Description

#' Usage: ggplot(...) + geom_X(...) + pointsPutX(...)

Usage

pointsPutX(x_range, x_section, y, col = NA)

Arguments

Value

ggplot object with new geom_point

helper S3 class, enabling print(some-plot_Table-object)

Description

helper S3 class, enabling print(some-plot_Table-object)

Usage

## S3 method for class 'PTXQC_table'
print(x, ...)

Arguments

Augment a ggplot with footer text

Description

Augment a ggplot with footer text

Usage

printWithFooter(gg_obj, bottom_left = NULL, bottom_right = NULL)

Arguments

Value

-

Class which can compute plots and generate mzQC output (usually for a single metric).

Description

Internally calls the workerFcn() , which computes the actual plots metric scores and supporting data (e.g. mzQC metrics) of the derived class; the resulting data is checked and stored in the members of this class

Arguments

Details

Reference class which is instanciated with a metric description and a worker function (at initialization time, i.e. in the package) and can produce plots and mzQC values (at runtime, when data is provided) using setData().

All derived classes need to implement a 'workerFcn()' function, which returns a list with elements: c("plots", "mzQC", "htmlTable", "qcScores", "title"), where 'plots' is required; all others are optional.

Fields

helpText

Description (lengthy) of the metric and plot elements

workerFcn

Function which generates a result (usually plots). Data is provided using setData().

plots

List of plots (after setData() was called)

htmlTable

A table for display in the HTML report (preferred over a plot in Html mode)

qcScores

Data.frame of scores from a qcMetric (computed within workerFcn())

mzQC

An named list of mzQC MzQCqualityMetric's (named by their fc.raw.file for runQuality or concatenated fc.raw.files for setQualities (e.g. "file 1;file4")) (valid after setData() was called)

qcCat

QC category (LC, MS, or prep)

qcName

Name of the qcScore in the heatmap

orderNr

Column index during heatmap generation and for the general order of plots

Examples

require(ggplot2)
dd = data.frame(x=1:10, y=11:20)
a = qcMetric$new(helpText="small help text", 
                 ## arbitrary arguments, matched during setData()
                 workerFcn=function(.self, data, gtitle)
                 {
                   ## usually some code here to produce ggplots
                   pl = lapply(1:2, function(xx) {
                       ggplot(data) +
                         geom_point(aes(x=x*xx,y=y)) +
                         ggtitle(gtitle)
                     })
                     ## add mzQC metric for count of identified clusters
                     mzqc = lapply(1:3, function(id){
                       out = rmzqc::getQualityMetricTemplate("MS:1002406")
                       out$value = id;
                       return(out) })
                     names(mzqc) = paste0("file", 1:3);
                   return(list(plots = pl, mzQC = mzqc))
                 }, 
                 qcCat="LC", 
                 qcName="MS/MS Peak shape", 
                 orderNr = 30)
## test some output
a$setData(dd, "my title")
a$plots  ## the raw plots
a$getPlots(TRUE) ## same as above
a$getPlots(FALSE) ## plots without title
a$getTitles()  ## get the titles of the all plots
a$helpText
a$qcName
a$mzQC

Metric for msmsscans.txt, showing TopN over RT.

Description

Metric for msmsscans.txt, showing TopN over RT.

From a list of vectors, compute all vs. all Kolmogorov-Smirnoff distance statistics (D)

Description

... and report the row of the matrix which has maximum sum (i.e the best "reference" distribution). The returned data.frame has as many rows as distributions given and two columns. The first column 'name' gives the name of the list element, the second column 'ks_best' gives '1-statistic' of the Kolmogorov-Smirnoff test to the "reference" distribution (which was picked by maximising the sum of 'ks_best'). Thus, the row with a 'ks_best' of 1 is the reference distribution.

Usage

qualBestKS(x)

Arguments

Value

A data.frame with ks-test values of the "reference" to all other distributions (see Details)

Quality metric for 'centeredness' of a distribution around zero.

Description

Ranges between 0 (worst score) and 1 (best score). A median of zero gives the best score of 1. The closer the median is to the most extreme value of the distribution, the smaller the score (until reaching 0). Can be used for calibrated mass errors, as a measure of how well they are centered around 0. E.g. if the median is 0.1, while the range is [-0.5,0.5], the score will be 0.8 (punishing the 20 If the range of data is asymmetric, e.g. [-1.5,-0.5] and does not include zero, the score cannot reach 1, since the median can never be zero.

Usage

qualCentered(x)

Arguments

Value

Value between [0, 1]

Quality metric for 'centeredness' of a distribution around zero with a user-supplied range threshold.

Description

Ranges between 0 (worst score) and 1 (best score). The best score is achieved when the median of 'x' is close to the center of the interval [-tol, tol]. If median of 'x' is close to the border (on either side), the score decreases linearly to zero. Can be used for uncalibrated mass errors, as a measure of how well they are centered around 0.

Usage

qualCenteredRef(x, tol)

Arguments

Details

NA's are removed for all computations.

Value

Value between [0, 1]

Compute probability of Gaussian (mu=m, sd=s) at a position 0, with reference to the max obtainable probability of that Gaussian at its center.

Description

Measure for centeredness around 0. Highest score is 1, worst score is 0.

Usage

qualGaussDev(mu, sd)

Arguments

Value

quality, ranging from 0 (bad agreement) to 1 (perfect, i.e. centered at 0)

Score an empirical density distribution of values, where the best possible distribution is right-skewed.

Description

The score is computed according to

Usage

qualHighest(x, N)

Arguments

Details

q = ((N-1) - sum_i( ((N-i-1)*x_i) ) / (N-1)

Scores range from 0 (worst), to 1 (best). E.g. c(0,0,0,16) would yield a score of 1. c(16,0,0,0,0) gives a score of 0.

Value

Quality score in the range of [0,1]

Examples

 qualHighest(c(0,0,0,16), 4)   ## 1
 qualHighest(c(16,0,0,0), 4)   ## 0 
 qualHighest(c(1,1,1,1), 4)    ## 0.5
 qualHighest(c(0,16,0,0), 4)   ## 1/3
 

Quality metric with linear response to input, reaching the maximum score at the given threshold.

Description

Ranges between 0 (worst score) and 1 (best score). Useful for performance measures where reaching a certain reference threshold 't' will be enough to reach 100%. The input range from [0, t] is scored from 0-100%.

Usage

qualLinThresh(x, t = 1)

Arguments

Value

Value between [0, 1]

Quality metric which measures the absolute distance from median.

Description

Ranges between 0 (worst score) and 1 (best score). Input must be between [0,1]. Deviations from the median of the sample represent the score for each sample point.

Usage

qualMedianDist(x)

Arguments

Value

A vector of the same size as x, with quality values between [0, 1]

Compute deviation from uniform distribution

Description

The score ranges between 0 (worst score) and 1 (best score). Input 'x' is a vector of counts (or probabilities) for equally spaced bins in a histogram. A uniform distribution (e.g. c(3,3,3) will get a score of 1. The worst possible case (e.g. c(4,0,0)), will get a score of 0, and a linear increasing function (e.g. c(1,2,3)) will get something in between (0.585 here)

Usage

qualUniform(x, weight = vector())

Arguments

Details

In addition, bin values can be weighted (e.g. by their confidence). The total sum of weights is normalized to 1 internally.

The distance function used is the square root of the absolute difference between a uniform distribution and the input 'x' (summed for each element of 'x'). This distance is normalized to the worst possible input (e.g. one bin with 100

Value

Value between [0, 1]

Examples

 stopifnot(qualUniform(c(3,3,3))==1)
 stopifnot(qualUniform(c(4,0,0))==0)         

 ## how 'uniform' is a vector where only a single index has weight?-- answer: very
 stopifnot(qualUniform(c(4,0,0), c(1,0,0))==1)   
 stopifnot(qualUniform(c(4,0,0), c(0,1,0))==1)     
 stopifnot(qualUniform(c(0,4,0))==0)              
 stopifnot(abs(qualUniform(c(3,2,1))-0.58578) < 0.0001)
 stopifnot(abs(qualUniform(c(1,2,3))-0.58578) < 0.0001)
 stopifnot(qualUniform(c(1,2,3), c(0,1,0))==1)   
 stopifnot(abs(qualUniform(c(1,2,3))-0.58578) < 0.0001)
 stopifnot(abs(qualUniform(c(1,2,3), c(0,1,1))- 0.590316) < 0.0001)
 stopifnot(abs(qualUniform(c(2,3), c(1,1))-0.552786) < 0.0001)
 stopifnot(abs(qualUniform(1:120)-0.38661) < 0.0001)
 

Convenience wrapper for MQDataReader when only a single MQ file should be read and file mapping need not be stored.

Description

For params, see MQDataReader::readMQ().

Usage

read.MQ(
  file,
  filter = "",
  type = "pg",
  col_subset = NA,
  add_fs_col = 10,
  LFQ_action = FALSE,
  ...
)

Arguments

Value

see MQDataReader::readMQ()

Given a vector of (short/long) filenames, translate to the (long/short) version

Description

Given a vector of (short/long) filenames, translate to the (long/short) version

Usage

renameFile(f_names, mapping)

Arguments

Value

A vector of translated file names as factor (ordered by mapping!)

Repeat each element x_i in X, n_i times.

Description

Repeat each element x_i in X, n_i times.

Usage

repEach(x, n)

Arguments

Value

Vector with values from x, n times

Examples

  repEach(1:3, 1:3)  ## 1, 2, 2, 3, 3, 3

Scales a vector of values linearly to [0, 1] If all input values are equal, returned values are all 0

Description

Scales a vector of values linearly to [0, 1] If all input values are equal, returned values are all 0

Usage

scale01linear(X)

Arguments

Value

Scaled vector

Inverse the order of items on the x-axis (for discrete scales)

Description

Inverse the order of items on the x-axis (for discrete scales)

Usage

scale_x_discrete_reverse(values, ...)

Arguments

Value

ggplot object, concatenatable with '+'

Inverse the order of items on the y-axis (for discrete scales)

Description

Inverse the order of items on the y-axis (for discrete scales)

Usage

scale_y_discrete_reverse(values, ...)

Arguments

Value

ggplot object, concatenatable with '+'

Shorten a string to a maximum length and indicate shorting by appending '..'

Description

Some axis labels are sometimes just too long and printing them will either squeeze the actual plot (ggplot) or make the labels disappear beyond the margins (graphics::plot) One ad-hoc way of avoiding this is to shorten the names, hoping they are still meaningful to the viewer.

Usage

shortenStrings(x, max_len = 20, verbose = TRUE, allow_duplicates = FALSE)

Arguments

Details

This function should be applied AFTER you tried more gentle methods, such as delLCP or simplifyNames.

Value

A vector of shortened strings

See Also

delLCP, simplifyNames

Examples

r = shortenStrings(c("gamg_101", "gamg_101230100451", "jurkat_06_100731121305", "jurkat_06_1"))
all(r == c("gamg_101", "gamg_101230100..", "jurkat_06_1007..", "jurkat_06_1"))

Removes common substrings (infixes) in a set of strings.

Description

Usually handy for plots, where condition names should be as concise as possible. E.g. you do not want names like 'TK20130501_H2M1_010_IMU008_CISPLA_E3_R1.raw' and 'TK20130501_H2M1_026_IMU008_CISPLA_E7_R2.raw' but rather 'TK.._010_I.._E3_R1.raw' and 'TK.._026_I.._E7_R2.raw'

If multiple such substrings exist, the algorithm will remove the longest first and iterate a number of times (two by default) to find the second/third etc longest common substring. Each substring must fulfill a minimum length requirement - if its shorter, its not considered worth removing and the iteration is aborted.

Usage

simplifyNames(
  strings,
  infix_iterations = 2,
  min_LCS_length = 7,
  min_out_length = 7
)

Arguments

Value

A list of shortened strings, with the same length as the input

Examples

#library(PTXQC)
simplifyNames(c('TK20130501_H2M1_010_IMU008_CISPLA_E3_R1.raw',
                'TK20130501_H2M1_026_IMU008_CISPLA_E7_R2.raw'), infix_iterations = 2)
# --> "TK.._010_I.._E3_R1.raw","TK.._026_I.._E7_R2.raw"

try(simplifyNames(c("bla", "foo"), min_LCS_length=5))
# --> error, since min_LCS_length must be >=6

Compute shortest prefix length which makes all strings in a vector uniquely identifyable.

Description

If there is no unique prefix (e.g. if a string is contained twice), then the length of the longest string is returned, i.e. if the return value is used in a call to substr, nothing happens e.g. substr(x, 1, supCount(x)) == x

Usage

supCount(x, prefix_l = 1)

Arguments

Value

Integer with minimal prefix length required

Examples

  supCount(c("abcde...", "abcd...", "abc..."))  ## 5

  x = c("doubled", "doubled", "aLongDummyString")
  all( substr(x, 1, supCount(x)) == x )   
  ## TRUE (no unique prefix due to duplicated entries)

A blank theme (similar to the deprecated theme_blank())

Description

A blank theme (similar to the deprecated theme_blank())

Usage

theme_blank()

Value

A ggplot2 object, representing an empty theme

Thin out a data.frame by removing rows with similar numerical values in a certain column.

Description

All values in the numerical column 'filterColname' are assigned to bins of width 'binsize'. Only one value per bin is retained. All other rows are removed and the reduced data frame will all its columns is returned.

Usage

thinOut(data, filterColname, binsize)

Arguments

Value

Data.frame with reduced rows, but identical input columns

Apply 'thinOut' on all subsets of a data.frame, split by a batch column

Description

The binsize is computed from the global data range of the filter column by dividing the range into binCount bins.

Usage

thinOutBatch(data, filterColname, batchColname, binCount = 1000)

Arguments

Value

Data.frame with reduced rows, but identical input columns

Check if a file is writable and blocks an interactive session, waiting for user input.

Description

This functions gives the user a chance to make the output file writeable before a write attempt is actually made by R to avoid having run the whole program again upon write failure.

Usage

wait_for_writable(
  filename,
  prompt_text = paste0("The file '", filename,
    "' is not writable. Please close all applications using this file. Press '",
    abort_answer, "' to abort!"),
  abort_answer = "n"
)

Arguments

Details

Note: The file will not be overwritten or changed by this function.

Value

TRUE if writable, FALSE if aborted by user or (not-writeable and non-interactive)