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#' Clean raw output from MaxQuant
#' @param msstats_object object that inherits from MSstatsInputFiles class.
#' @param protein_id_col character, name of a column with names of proteins.
#' @param remove_by_site logical, if TRUE, proteins only identified by site will
#' be removed.
#' @param channel_columns character, regular expression that identifies channel columns
#' in TMT data.
#' @return data.table
#' @keywords internal
.cleanRawMaxQuant = function(msstats_object, protein_id_col,
remove_by_site = FALSE,
channel_columns = "Reporterintensitycorrected"
) {
ProteinIDs = id = PSM = PeptideSequence = NULL
ProteingroupIDs = PrecursorCharge = Intensity = NULL
mq_input = getInputFile(msstats_object, "evidence")
mq_pg = getInputFile(msstats_object, "protein_groups")
filter_cols = c("Contaminant", "Potentialcontaminant", "Reverse")
msg = paste("** + Contaminant, + Reverse, + Potential.contaminant",
"proteins are removed.")
if (remove_by_site) {
filter_cols = c(filter_cols, "Onlyidentifiedbysite")
msg = paste("** + Contaminant, + Reverse, + Potential.contaminant,",
"+ Only.identified.by.site proteins are removed.")
}
mq_input = .filterManyColumns(mq_input, filter_cols, "+")
mq_pg = .filterManyColumns(mq_pg, filter_cols, "+")
getOption("MSstatsLog")("INFO", msg)
getOption("MSstatsMsg")("INFO", msg)
suppressWarnings({
mq_input[, ProteingroupIDs := as.integer(as.character(ProteingroupIDs))]
})
if (getDataType(msstats_object) == "MSstats") {
mq_input = mq_input[ProteingroupIDs %in% unique(mq_pg[["id"]]), ]
}
mq_input = merge(mq_input,
unique(mq_pg[, list(uniquefromProteinGroups = ProteinIDs,
ProteingroupIDs = id)]),
by = "ProteingroupIDs", sort = FALSE)
if (getDataType(msstats_object) == "MSstatsTMT") {
protein_id = .findAvailable(c("Proteins", "Leadingproteins",
"Leadingrazorprotein", "Genenames"),
colnames(mq_input), "Proteins")
channels = .getChannelColumns(colnames(mq_input), channel_columns)
} else {
protein_id = ifelse(protein_id_col == "Proteins",
"uniquefromProteinGroups",
"Leadingrazorprotein")
channels = character(0)
}
data.table::setnames(
mq_input,
c(protein_id, "Modifiedsequence", "Charge", "Rawfile"),
c("ProteinName", "PeptideSequence", "PrecursorCharge", "Run"),
skip_absent = TRUE)
mq_input[["PeptideSequence"]] = gsub("_", "", mq_input[["PeptideSequence"]])
mq_cols = c("ProteinName", "PeptideSequence", "Modifications",
"PrecursorCharge", "Run", "Intensity",
"Fraction", "TechReplicate", "Run", "BioReplicate",
"PSM", "Score")
mq_cols = intersect(c(mq_cols, channels),
colnames(mq_input))
mq_input = mq_input[, mq_cols, with = FALSE]
mq_input = unique(mq_input)
if (getDataType(msstats_object) == "MSstatsTMT") {
mq_input[, PSM := paste(PeptideSequence, PrecursorCharge,
1:nrow(mq_input), sep = "_")]
mq_input = melt(mq_input, measure.vars = channels,
id.vars = c("ProteinName", "PeptideSequence",
"PrecursorCharge", "PSM", "Run", "Score"),
variable.name = "Channel", value.name = "Intensity")
mq_input$Channel = gsub(channel_columns, "channel", mq_input$Channel)
mq_input$Channel = .standardizeColnames(mq_input$Channel)
suppressWarnings({
mq_input$Intensity = ifelse(mq_input$Intensity == 0, NA,
mq_input$Intensity)
})
mq_input = .filterFewMeasurements(mq_input, 0, FALSE,
c("PeptideSequence",
"PrecursorCharge", "Run"))
}
mq_input = mq_input[!is.na(Intensity), ]
.logSuccess("MaxQuant", "clean")
mq_input
}
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