Analysis of the Relative Quantifications — artmsAnalysisQuantifications • artMS
Description: Analysis of relative quantifications, including: Annotations Summary files in different format (xls, txt) and shapes (long, wide) Numerous summary plots Enrichment analysis using Gprofiler PCA of quantifications Clustering analysis Basic imputation of missing values To run this function, the following packages must be installed on your system: From bioconductor: BiocManager::install(c("ComplexHeatmap", "org.Mm.eg.db")) From CRAN: install.packages(c("factoextra", "FactoMineR", "gProfileR", "PerformanceAnalytics"))

# Analysis of the Relative Quantifications

Source: `R/analysisQuantifications.R`
`artmsAnalysisQuantifications.Rd`

Analysis of relative quantifications, including:

- Annotations

- Summary files in different format (xls, txt) and shapes (long, wide)

- Numerous summary plots

- Enrichment analysis using Gprofiler

- PCA of quantifications

- Clustering analysis

- Basic imputation of missing values

To run this function, the following packages must be installed on your system:

- From bioconductor: `BiocManager::install(c("ComplexHeatmap", "org.Mm.eg.db"))`

- From CRAN: `install.packages(c("factoextra", "FactoMineR", "gProfileR", "PerformanceAnalytics"))`

    artmsAnalysisQuantifications(
      log2fc_file,
      modelqc_file,
      species,
      output_dir = "analysis_quant",
      outliers = c("keep", "iqr", "std"),
      enrich = TRUE,
      l2fc_thres = 1,
      choosePvalue = c("adjpvalue", "pvalue"),
      isBackground = "nobackground",
      isPtm = "global",
      mnbr = 2,
      pathogen = "nopathogen",
      plotPvaluesLog2fcDist = TRUE,
      plotAbundanceStats = TRUE,
      plotReproAbundance = TRUE,
      plotCorrConditions = TRUE,
      plotCorrQuant = TRUE,
      plotPCAabundance = TRUE,
      plotFinalDistributions = TRUE,
      plotPropImputation = TRUE,
      plotHeatmapsChanges = TRUE,
      plotTotalQuant = TRUE,
      plotClusteringAnalysis = TRUE,
      data_object = FALSE,
      printPDF = TRUE,
      verbose = TRUE
    )

## Arguments

log2fc_file | (char) MSstats results file location
modelqc_file | (char) MSstats modelqc file location
species | (char) Select one species. Species currently supported for a full analysis (including enrichment analysis): HUMAN MOUSE To find out species supported only for annotation check `?artmsIsSpeciesSupported()`
output_dir | (char) Name for the folder to output the results from the function. Default is current directory (recommended to provide a new folder name).
outliers | (char) It allows to keep or remove outliers. Options: `keep` (default): it keeps outliers 'keep', 'iqr', 'std' `iqr` (recommended): remove outliers +/- 6 x Interquartile Range (IQR) `std` : 6 x standard deviation
enrich | (logical) Performed enrichment analysis using GprofileR? Only available for species HUMAN and MOUSE. `TRUE` (default if "human" or "mouse" are the species) or `FALSE`
l2fc_thres | (int) log2fc cutoff for enrichment analysis (default, `l2fc_thres = 1.5`)
choosePvalue | (char) specify whether `pvalue` or `adjpvalue` should use for the analysis. The default option is `adjpvalue` (multiple testing correction). But if the number of biological replicates for a given experiment is too low (for example n = 2), then `choosePvalue = pvalue` is recommended.
isBackground | (char) background of gene names for enrichment analysis. `nobackground` (default) will use the total number of genes detected. Alternatively provided the file path name to the background gene list.
isPtm | (char) Is a ptm-site quantification? `global` (default), `ptmsites` (for site specific analysis), `ptmph` (Jeff Johnson script output evidence file)
mnbr | (int) PARAMETER FOR NAIVE IMPUTATION: "minimal number of biological replicates" for "naive imputation" and filtering. Default: `mnbr = 2`. Details: Intensity values for proteins/PTMs that are completely missed in one of the two conditions compared ("condition A"), but are found in at least 2 biological replicates (`mnbr = 2`) of the other "condition B", are imputed (values artificially assigned) and the log2FC values calculated. The goal is to keep those proteins/PTMs that are consistently found in one of the two conditions (in this case "condition B") and facilitate the inclusion in downstream analysis (if wished). The imputed intensity values are sampled from the lowest intensity values detected in the experiment, and (WARNING) the p-values are just randomly assigned between 0.05 and 0.01 for illustration purposes (when generating a volcano plot with the output of `artmsAnalysisQuantifications`) or to include them when making a cutoff of p-value < 0.05 for enrichment analysis CAUTION: `mnbr` would also add the constraint that any protein must be identified in at least `nmbr` biological replicates of the same condition or it will be filtered out. That is, if `mnbr = 2`, a protein found in two conditions but only in one biological replicate in each of them, it would be removed.
pathogen | (char) Is there a pathogen in the dataset as well? if it does not, then use `pathogen = nopathogen` (default). Pathogens available: `tb` (Tuberculosis), `lpn` (Legionella)
plotPvaluesLog2fcDist | (logical) If `TRUE` (default) plots pvalues and log2fc distributions
plotAbundanceStats | (logical) If `TRUE` (default) plots stats graphs about abundance values
plotReproAbundance | (logical) If `TRUE` plots reproducibility based on normalized abundance values
plotCorrConditions | (logical) If `TRUE` plots correlation between the different conditions
plotCorrQuant | (logical) if `TRUE` plots correlation between the available quantifications (comparisons)
plotPCAabundance | (logical) if `TRUE` performs PCA analysis of conditions using normalized abundance values
plotFinalDistributions | (logical) if `TRUE` plots distribution of both log2fc and pvalues
plotPropImputation | (logical) if `TRUE` plots proportion of overall imputation
plotHeatmapsChanges | (logical) if `TRUE` plots heatmaps of quantified changes (both all and significant only). Only if `printPDF` is also `TRUE`
plotTotalQuant | (logical) if `TRUE` plots barplot of total number of quantifications per comparison
plotClusteringAnalysis | (logical) if `TRUE` performs clustering analysis between quantified comparisons (more than 1 comparison required)
data_object | (logical) flag to indicate whether the required files are data objects. Default is FALSE
printPDF | If `TRUE` (default) prints out the pdfs. Warning: plot objects are not returned due to the large number of them.
verbose | (logical) `TRUE` (default) shows function messages

## Value

(data.frame) summary of quantifications, including annotations, enrichments, etc

## Examples

    # Testing that the files cannot be empty
    artmsAnalysisQuantifications(log2fc_file = NULL,
                                  modelqc_file = NULL,
                                  species = NULL,
                                  output_dir = NULL)
    #> ---------------------------------------------#> artMS: ANALYSIS OF QUANTIFICATIONS#> ---------------------------------------------#> [1] "The evidence_file, modelqc_file, species and output_dir arguments cannot be NULL"