| Type: | Package |
| Title: | Targeted Differential and Global Enrichment Analysis of Taxonomic Rank by Shared Asvs |
| Version: | 0.1.5 |
| Author: | Pierre-Louis Stenger [cre, aut] |
| Maintainer: | Pierre-Louis Stenger <Pierrelouis.stenger@gmail.com> |
| Description: | Targeted differential and global enrichment analysis of taxonomic rank by shared ASVs (Amplicon Sequence Variant), for high-throughput eDNA sequencing of fungi, bacteria, and metazoan. Actually works in two steps: I) Targeted differential analysis from QIIME2 data and II) Global analysis by Taxon Mann-Whitney U test analysis from targeted analysis (I) (I) Estimate variance-mean dependence in count/abundance ASVs data from high-throughput sequencing assays and test for differential represented ASVs based on a model using the negative binomial distribution. (II) NCBITaxon_MWU uses continuous measure of significance (such as fold-change or -log(p-value)) to identify NCBITaxon that are significantly enriches with either up- or down-represented ASVs. If the measure is binary (0 or 1) the script will perform a typical 'NCBITaxon enrichment' analysis based Fisher's exact test: it will show NCBITaxon over-represented among the ASVs that have 1 as their measure. On the plot, different fonts are used to indicate significance and color indicates enrichment with either up (red) or down (blue) regulated ASVs. No colors are shown for binary measure analysis. The tree on the plot is hierarchical clustering of NCBITaxon based on shared ASVs. Categories with no branch length between them are subsets of each other. The fraction next to the category name indicates the fraction of 'good' ASVs in it; 'good' ASVs are the ones exceeding the arbitrary absValue cutoff (option in taxon_mwuPlot()). For Fisher's based test, specify absValue=0.5. This value does not affect statistics and is used for plotting only. The original idea was for genes differential expression analysis from Wright et al (2015) <doi:10.1186/s12864-015-1540-2>; adapted here for taxonomic analysis. The 'Anaconda' package makes it possible to carry out these analyses by automatically creating several graphs and tables and storing them in specially created subfolders. You will need your QIIME2 pipeline output for each kingdom (eg; Fungi and/or Bacteria and/or Metazoan): i) taxonomy.tsv, ii) taxonomy_RepSeq.tsv, iii) ASV.tsv and iv) SampleSheet_comparison.txt (the latter being created by you). |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/PLStenger/Anaconda |
| BugReports: | https://github.com/PLStenger/Anaconda/issues |
| Imports: | ggrepel, pheatmap, lookup, plyr, data.table, rafalib, RColorBrewer, methods, graphics |
| Depends: | ape, DESeq2, ggplot2 |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.2.1 |
| NeedsCompilation: | no |
| Packaged: | 2022-10-13 08:09:09 UTC; pierre-louisstenger |
| Repository: | CRAN |
| Date/Publication: | 2022-10-14 08:55:29 UTC |
Bacteria
Description
This function create a new folder named Bacteria and set your working directory into this folder. Please, run setwd("Bacteria") after this function.
Usage
Bacteria(nothing)
Arguments
nothing |
It's important not to write anything between the brackets, a new folder named Bacteria will be created and your working directory will be set into this folder, depending of the selected Kingdom. |
Value
A new folder named Bacteria will be created and your working directory will be set into this folder, depending of the selected Kingdom.
Examples
## Not run: Bacteria()
# Please, run setwd("Bacteria") after this function.
Fungi
Description
This function create a new folder named Fungi and set your working directory into this folder. Please, run setwd("Fungi") after this function.
Usage
Fungi(nothing)
Arguments
nothing |
It's important not to write anything between the brackets, a new folder named Fungi will be created and your working directory will be set into this folder, depending of the selected Kingdom. |
Value
A new folder named Fungi will be created and your working directory will be set into this folder, depending of the selected Kingdom.
Examples
## Not run: Fungi()
# plaese, run setwd("Fungi") after this function.
Metazoan
Description
This function create a new folder named Metazoan and set your working directory into this folder. Please, run setwd("Metazoan") after this function.
Usage
Metazoan(nothing)
Arguments
nothing |
It's important not to write anything between the brackets, a new folder named Metazoan will be created and your working directory will be set into this folder, depending of the selected Kingdom. |
Value
A new folder named Metazoan will be created and your working directory will be set into this folder, depending of the selected Kingdom.
Examples
## Not run: Metazoan()
# plaese, run setwd("Metazoan") after this function.
PCA_data_dasva
Description
Compute the PCA (Pincipal Component Analysis) data.
Usage
PCA_data_dasva(nothing)
Arguments
nothing |
It's important not to write anything between the brackets, all inputs will be adapted automatically. |
Value
data. The PCA (Pincipal Component Analysis) data.
clusteringGOs
Description
clusteringGOs from DESeq2 analysis pipeline
Usage
clusteringGOs(gen2go, div, cutHeight)
Arguments
gen2go |
from DESeq2 analysis pipeline |
div |
div |
cutHeight |
cutHeight |
Value
a clustering GO
Examples
## Not run: clusteringGOs()
dasva_raw_input
Description
Used in heatmap_samples_hclust(), heatmap_samples_matrix(), PCA_data_dasva() and get_dasva() functions.
Usage
dasva_raw_input(sampleTable, directory = ".", design, ignoreRank = FALSE, ...)
Arguments
sampleTable |
Depending of the heatmap_samples_hclust(), heatmap_samples_matrix(), PCA_data_dasva() and get_dasva() functions. |
directory |
directory |
design |
design |
ignoreRank |
ignoreRank |
... |
... |
Value
object
Examples
## Not run: dasva_raw <- dasva_raw_input(sampleTable = sampleTable,
directory = targeted_analysis_dir,
design= ~ condition)
## End(Not run)
database_bacteria_creation
Description
Create a Database for Bacteria kingdom for Global analysis by Taxon_MWU analysis from targeted analysis. Please, run setwd("02_Global_analysis") after this function.
Usage
database_bacteria_creation(nothing)
Arguments
nothing |
It's important not to write anything between the brackets, the database will create itself. |
Value
A data frame file named database_bacteria_package_all.tab created from the taxonomy_all_bacteria_QIIME2_and_NCBI_format.txt file and your own taxonomy_RepSeq.tsv file. database_bacteria_creation()
Examples
# It is important not to write anything between the brackets, the database will create itself.
## Not run: database_bacteria_creation()
# Please, run setwd("02_Global_analysis") after this function.
database_fungi_creation
Description
Create a Database for Fungi kingdom for Global analysis by Taxon_MWU analysis from targeted analysis only from rarefied ASVs. Please, run setwd("02_Global_analysis") after this function.
Usage
database_fungi_creation(nothing)
Arguments
nothing |
It's important not to write anything between the brackets, the database will create itself. |
Value
A data frame file named database_fungi_package_all.tab created from the taxonomy_all_bacteria_QIIME2_and_NCBI_format.txt file and your own taxonomy.tsv file.
Examples
# It is important not to write anything between the brackets, the database will create itself.
## Not run: database_fungi_creation()
# Please, run setwd("02_Global_analysis") after this function.
database_fungi_creation_RepSeq
Description
Create a Database for Fungi kingdom for Global analysis by Taxon_MWU analysis from targeted analysis. Please, run setwd("02_Global_analysis") after this function.
Usage
database_fungi_creation_RepSeq(nothing)
Arguments
nothing |
It's important not to write anything between the brackets, the database will create itself. |
Value
A data frame file named database_fungi_package_all.tab created from the taxonomy_all_bacteria_QIIME2_and_NCBI_format.txt file and your own taxonomy_RepSeq.tsv file.
Examples
# It is important not to write anything between the brackets, the database will create itself.
## Not run: database_fungi_creation_RepSeq()
# Please, run setwd("02_Global_analysis") after this function.
database_metazoan_creation
Description
Create a Database for metazoan kingdom for Global analysis by Taxon_MWU analysis from targeted analysis. Please, run setwd("02_Global_analysis") after this function.
Usage
database_metazoan_creation(nothing)
Arguments
nothing |
It's important not to write anything between the brackets, the database will create itself. |
Value
A data frame file named database_metazoan_package_all.tab created from the taxonomy_all_metazoan_QIIME2_and_NCBI_format.txt file and your own taxonomy_RepSeq.tsv file. database_metazoan_creation()
Examples
# It is important not to write anything between the brackets, the database will create itself.
## Not run: database_metazoan_creation()
# Please, run setwd("02_Global_analysis") after this function.
fisherTest
Description
Ficher Test from RBGOA
Usage
fisherTest(gotable)
Arguments
gotable |
from gomwuStats from RBGOA |
Value
fisherTest
Examples
## Not run: fisherTest()
format_input
Description
Apply logP on both positive and negative ASVs FC
Usage
format_input(x)
Arguments
x |
Object from the Differential ASV abundance (DASVA) analysis |
Value
an input for the input_global_analysis() function
Examples
## Not run: format_input(x)
funguild_input_targeted
Description
Prepare Object for Fungi Guilds for Fungi kingdom for targeted analysis
Usage
funguild_input_targeted(x)
Arguments
x |
Object from the Differential ASV abundance (DASVA) analysis |
Value
An Object used for Fungi Guilds informations for Fungi kingdom for targeted analysis from the Differential ASV abundance (DASVA) analysis
Examples
## Not run: get_funguilds_targeted(res_forest_vs_long_fallow_guilds)
get_bactotraits_targeted
Description
Obtain Bacterial Traits for Bacteria kingdom for targeted analysis
Usage
get_bactotraits_targeted(x)
Arguments
x |
Object from the Differential ASV abundance (DASVA) analysis |
Value
A data frame file with Bacterial Traits informations for Bacteria kingdom for targeted analysis from the Differential ASV abundance (DASVA) analysis
Examples
## Not run: get_bactotraits_targeted(res_forest_vs_long_fallow)
get_dasva
Description
Creates the DASVA object. Fit a Gamma-Poisson Generalized Linear Model, dispersion estimates for Negative Binomial distributed data, "parametric", local" or "mean"
Usage
get_dasva(fitType = "")
Arguments
fitType |
Fit a Gamma-Poisson Generalized Linear Model, dispersion estimates for Negative Binomial distributed data, "parametric", local" or "mean" |
Value
DASVA object
Examples
## Not run: dasva <- get_dasva(fitType="parametric")
dasva <- get_dasva(fitType="local")
dasva <- get_dasva(fitType="mean")
## End(Not run)
get_funguilds
Description
get Fungi Guilds from taxon_list_drawer Object
Usage
get_funguilds(taxon_list_drawer)
Arguments
taxon_list_drawer |
object from get_taxon_list_drawer() function |
Value
funguilds Object
Examples
## Not run: funguilds <- get_funguilds(taxon_list_drawer)
get_funguilds_targeted
Description
Obtain Fungi Guilds for Fungi kingdom for targeted analysis
Usage
get_funguilds_targeted(x)
Arguments
x |
Object from the funguild_input_targeted() output. |
Value
A data frame file with Fungi Guilds informations for Fungi kingdom for targeted analysis from the Differential ASV abundance (DASVA) analysis
Examples
## Not run: get_funguilds_targeted(res_forest_vs_long_fallow_guilds)
get_input_files
Description
Created sub directory "Targeted_analysis" if not already exist. Then, create one file by condition into it, and then upload the taxonomy file. Please, run setwd("01_Targeted_analysis") after this function.
Usage
get_input_files(nothing)
Arguments
nothing |
It's important not to write anything between the brackets, all inputs will be adapted automatically. |
Value
taxo
Examples
## Not run: taxo <- get_input_files()
# please, run setwd("01_Targeted_analysis") after this function.
get_link_guilds
Description
get link guilds from taxon_list and funguilds Objects
Usage
get_link_guilds(taxon_list, funguilds)
Arguments
taxon_list |
object from taxon_mwu_list() function |
funguilds |
object from get_funguilds() function |
Value
link_guilds Object
Examples
## Not run: link_guilds <- get_link_guilds(taxon_list, funguilds)
get_taxon_list_drawer
Description
get taxonomic list drawer
Usage
get_taxon_list_drawer(taxon_list)
Arguments
taxon_list |
object from taxon_mwu_list() function |
Value
taxon_list_drawer Object and "taxon_list_drawer_input.txt" file
Examples
## Not run: taxon_list_drawer <- get_taxon_list_drawer(taxon_list)
heatmap_condition
Description
For Clustering step. Fill directly the annotation_col variable of the pheatmap() function
Usage
heatmap_condition(nothing)
Arguments
nothing |
It's important not to write anything between the brackets, all inputs will be adapted automatically. |
Value
Fill directly the annotation_col variable of the pheatmap() function
heatmap_data_dasva
Description
For Clustering step. Create the log2.norm.counts object.
Usage
heatmap_data_dasva(nothing)
Arguments
nothing |
It's important not to write anything between the brackets, all inputs will be adapted automatically. |
Value
Create the log2.norm.counts object.
heatmap_samples_hclust
Description
Adapt hclust for heatmap sample to sample analysis
Usage
heatmap_samples_hclust(nothing)
Arguments
nothing |
It's important not to write anything between the brackets, all inputs will be adapted automatically. |
Value
hclust object for the heatmap.2() function
Examples
## Not run: hc <- heatmap_samples_hclust()
heatmap_samples_matrix
Description
Adapt samples matrix for heatmap sample to sample analysis
Usage
heatmap_samples_matrix(nothing)
Arguments
nothing |
It's important not to write anything between the brackets, all inputs will be adapted automatically. |
Value
samples matrix object for the heatmap.2() function
Examples
## Not run: mat <- heatmap_samples_matrix()
heatmap_taxo
Description
Adding taxonomy in the pheatmap plot, instead of ASVs codes
Usage
heatmap_taxo(nothing)
Arguments
nothing |
It's important not to write anything between the brackets, all inputs will be adapted automatically. |
Value
log2.norm.counts_taxo used fro adding taxonomy in the pheatmap plot, instead of ASVs codes
Examples
## Not run: log2.norm.counts_taxo <- heatmap_taxo()
input_global_analysis
Description
Input files creation for each condition for Global analysis by Taxon_MWU analysis from targeted analysis (I)
Usage
input_global_analysis(x)
Arguments
x |
Object from the Differential ASV abundance (DASVA) analysis |
Value
Input Object for Global analysis by Taxon_MWU analysis from targeted analysis (I)
Examples
## Not run: input_global_analysis(res_forest_vs_long_fallow)
move_files
Description
Move the file in the good folders. Depending on the previous Kingdom selection (e.g., Fungi 'Fungi()', Bacteria 'Bacteria()', etc.)
Usage
move_files(nothing)
Arguments
nothing |
It's important not to write anything between the brackets, files will move in the good folders, depending of the selected Kingdom before. |
Value
Move the file in the good folders.
Examples
## Not run: move_files()
mwuTest
Description
Mann-Whitney U Test from RBGOA
Usage
mwuTest(gotable, Alternative)
Arguments
gotable |
from gomwuStats from RBGOA |
Alternative |
from gomwuStats from RBGOA |
Value
mwuTest
Examples
## Not run: mwuTest()
plotDispASVs
Description
Create a plot of Dispersion ASV
Usage
plotDispASVs(
object,
ymin,
CV = FALSE,
genecol = "black",
fitcol = "red",
finalcol = "dodgerblue",
legend = TRUE,
xlab,
ylab,
log = "xy",
cex = 0.45,
...
)
Arguments
object |
Corresponding to the DASVA (Differential ASV abundance) object |
ymin |
ymin |
CV |
CV |
genecol |
genecol |
fitcol |
fitcol |
finalcol |
finalcol |
legend |
legend |
xlab |
xlab |
ylab |
ylab |
log |
log |
cex |
cex |
... |
... |
Value
A plot of Dispersion ASV
Examples
## Not run: plotDispASVs(dasva)
plotMA.dasva
Description
Custom MA plots for the Differential ASV abundance (DASVA) analysis. defining a new function to plot all ASVs and not only log2FoldChange > 2
Usage
plotMA.dasva(
object,
alpha,
main = "",
xlab = "mean of normalized counts",
ylim,
MLE = FALSE,
verbose = TRUE,
...
)
Arguments
object |
Object from the Differential ASV abundance (DASVA) analysis |
alpha |
alpha |
main |
main |
xlab |
xlab |
ylim |
ylim |
MLE |
MLE |
verbose |
verbose |
... |
... |
Value
A MA plot
Examples
## Not run: plotMA.dasva(rXXX, main="XXX", ylim=c(-20,20))
plotPCA.san
Description
Custom plotPCA function to plot PC1 et PC3
Usage
plotPCA.san(object, intgroup = "condition", ntop = 500, returnData = FALSE)
Arguments
object |
An object use for the PCA |
intgroup |
intgroup |
ntop |
ntop |
returnData |
returnData |
Value
A PCA
Examples
## Not run: plotPCA.san(object)
plotSparsityASV
Description
Create a plot of Sparsity ASV
Usage
plotSparsityASV(x, normalized = TRUE, ...)
Arguments
x |
Corresponding to the DASVA (Differential ASV abundance) object |
normalized |
normalized |
... |
... |
Value
A plot of Sparsity ASV
Examples
## Not run: plotSparsityASV(dasva)
samplesInfo
Description
Imports conditions information from your SampleSheet_comparison.txt file, with focus on samplesInfo.
Usage
samplesInfo(nothing)
Arguments
nothing |
It's important not to write anything between the brackets, comparisons will create themselves. |
Value
a data.frame with conditions information from your SampleSheet_comparison.txt file, with focus on samplesInfo.
Examples
## Not run: samplesInfo <- samplesInfo()
target_file
Description
Imports conditions information from your SampleSheet_comparison.txt file, with focus on iput files.
Usage
target_file(nothing)
Arguments
nothing |
It's important not to write anything between the brackets, comparisons will create themselves. |
Value
a data.frame with conditions information from your SampleSheet_comparison.txt file
Examples
## Not run: target_file <- target_file()
taxon_mwuPlot
Description
taxon mwuPlot for taxonomic analysis
Usage
taxon_mwuPlot(
inFile,
goAnnotations,
goDivision,
level1 = 0.1,
level2 = 0.05,
level3 = 0.01,
absValue = -log(0.05, 10),
adjusted = TRUE,
txtsize = 1,
font.family = "sans",
treeHeight = 0.5,
colors = NULL,
verbose = TRUE
)
Arguments
inFile |
inFile - results object from the DASVA analysis |
goAnnotations |
parallel to goAnnotations from gomwuStats from RBGOA. Here, "database_bacteria_package_all.tab" if Bacteria, "database_fungi_package_all.tab" if Fungi |
goDivision |
parallel to goAnnotations from gomwuStats from RBGOA. Here, "TR" = taxonomic Rank, don't change this |
level1 |
level1 |
level2 |
level2 |
level3 |
level3 |
absValue |
absValue |
adjusted |
adjusted |
txtsize |
txtsize |
font.family |
font.family |
treeHeight |
treeHeight |
colors |
colors |
verbose |
verbose |
Value
taxon mwuPlot and goods "Table_02_taxon_mwuPlot.txt"
Examples
## Not run: taxon_mwuPlot(input,...)
taxon_mwuPlot_guilds
Description
taxon Mann-Whitney U Plot with Fungi Guilds added
Usage
taxon_mwuPlot_guilds(
inFile,
goAnnotations,
goDivision,
level1 = 0.1,
level2 = 0.05,
level3 = 0.01,
absValue = -log(0.05, 10),
adjusted = TRUE,
txtsize = 1,
font.family = "sans",
treeHeight = 0.5,
colors = NULL,
verbose = TRUE
)
Arguments
inFile |
inFile - results object from the DASVA analysis |
goAnnotations |
parallel to goAnnotations from gomwuStats from RBGOA. Here, "database_bacteria_package_all.tab" if Bacteria, "database_fungi_package_all.tab" if Fungi |
goDivision |
parallel to goAnnotations from gomwuStats from RBGOA. Here, "TR" = taxonomic Rank, don't change this |
level1 |
level1 |
level2 |
level2 |
level3 |
level3 |
absValue |
absValue |
adjusted |
adjusted |
txtsize |
txtsize |
font.family |
font.family |
treeHeight |
treeHeight |
colors |
colors |
verbose |
verbose |
Value
List for the statistical analysis for taxonomic rank
Examples
## Not run: taxon_mwuPlot_guilds(input, ...)
taxon_mwuStats
Description
mwuStats from RBGOA adapted for taxonomic analysis
Usage
taxon_mwuStats(
input,
goDatabase,
goAnnotations,
goDivision,
Alternative = "t",
adjust.multcomp = "BH",
clusterCutHeight = 0.25,
largest = 0.1,
smallest = 5,
perlPath = "perl",
verbose = TRUE
)
Arguments
input |
input |
goDatabase |
goDatabase |
goAnnotations |
goAnnotations |
goDivision |
goDivision |
Alternative |
Alternative |
adjust.multcomp |
adjust.multcomp |
clusterCutHeight |
clusterCutHeight |
largest |
largest |
smallest |
smallest |
perlPath |
perlPath |
verbose |
verbose |
Value
Statistical analysis for taxonomic rank
Examples
## Not run: taxon_mwuStats(input, ...)
taxon_mwu_list
Description
taxon Mann-Whitney U list for taxonomic analysis
Usage
taxon_mwu_list(
inFile,
goAnnotations,
goDivision,
level1 = 0.1,
level2 = 0.05,
level3 = 0.01,
absValue = -log(0.05, 10),
adjusted = TRUE,
txtsize = 1,
font.family = "sans",
treeHeight = 0.5,
colors = NULL
)
Arguments
inFile |
inFile - results object from the DASVA analysis |
goAnnotations |
parallel to goAnnotations from gomwuStats from RBGOA. Here, "database_bacteria_package_all.tab" if Bacteria, "database_fungi_package_all.tab" if Fungi |
goDivision |
parallel to goAnnotations from gomwuStats from RBGOA. Here, "TR" = taxonomic Rank, don't change this |
level1 |
level1 |
level2 |
level2 |
level3 |
level3 |
absValue |
absValue |
adjusted |
adjusted |
txtsize |
txtsize |
font.family |
font.family |
treeHeight |
treeHeight |
colors |
colors |
Value
List for the statistical analysis for taxonomic rank
Examples
## Not run: taxon_list <- taxon_mwu_list(input, ...)