Read and Process Proteomics Experiment Data

readProteomeExperiment reads and processes proteomics data from multiple samples, applying various quality filters, and returns a SummarizedExperiment object.

Usage

readProteomeExperiment(
  fileTable,
  fdrCut = 0.1,
  scoreCut = 10,
  pepNumCut = 1,
  ifLFQ = TRUE
)

Arguments

fileTable

A data.table or data.frame containing the file information with columns for file names, sample names, and IDs.

fdrCut

A numeric value specifying the maximum false discovery rate (FDR) threshold. Default is 0.1.

scoreCut

A numeric value specifying the minimum score threshold. Default is 10.

pepNumCut

A numeric value specifying the minimum number of peptides required for a protein to be included. Default is 1.

ifLFQ

A logical value indicating whether to use LFQ quantification. Default is TRUE.

Value

A SummarizedExperiment object containing the processed proteomics data.

Details

This function processes proteomics data by filtering based on FDR, score, and peptide count, and optionally using LFQ quantification. It aggregates the data from multiple samples and constructs a SummarizedExperiment object.

Examples

file1 <- system.file("extdata", "phosDDA_1.xls", package = "SmartPhos")
file2 <- system.file("extdata", "proteomeDDA_1.xls", package = "SmartPhos")
# Create fileTable
fileTable <- data.frame(
   searchType = c("phosphoproteome", "proteome"),
   fileName = c(file1, file2),
   sample = c("Sample1", "sample1"),
   id = c("s1", "s2")
)
# Call the function
readProteomeExperiment(fileTable, fdrCut = 0.1, scoreCut = 10,
pepNumCut = 1, ifLFQ = TRUE)
#> class: SummarizedExperiment 
#> dim: 2 1 
#> metadata(0):
#> assays(1): Intensity
#> rownames(2): p1 p2
#> rowData names(3): UniprotID Gene PeptideCounts
#> colnames(1): s2
#> colData names(0):