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#' Filter OpenSWATH output table based on mscore.
#'
#' This function filters the SWATH/DIA data according to a m_score value, as well
#' as to the number of occurence in the data (requant) and within a condition
#' (condition).
#'
#' @param data A data frame containing SWATH data.
#' @param mscore Value that defines the mscore threshold according to which the
#' data will be filtered.
#' @param rm.decoy Option to drop decoys from the data
#' @param mscore.col Defines the column from which to retrieve the m_score.
#' If you use JPP (Rosenberger, Bludau et al. 2017) this can be used to
#' select between Protein and transition_group m_score.
#' @return Returns a data frame with the filtered data
#' @author Peter Blattmann
#' @examples
#' data("OpenSWATH_data", package="SWATH2stats")
#' data("Study_design", package="SWATH2stats")
#' data <- sample_annotation(OpenSWATH_data, Study_design)
#' data.filtered <- filter_mscore(data, 0.01)
#' data.filtered <- filter_mscore_freqobs(data, 0.01, 0.8)
#' data.filtered <- filter_mscore_condition(data, 0.01, 3)
#' @export
filter_mscore <- function(data,
mscore,
rm.decoy = TRUE,
mscore.col = "m_score") {
if (sum(colnames(data) == "decoy") == 1 & rm.decoy == TRUE) {
data <- data[data$decoy == 0, ]
# subset(data, decoy == 0)
}
mscore.col <- JPP_update(data, mscore.col)
data.filtered <- data[data[, mscore.col] <= mscore, ]
message("Dimension difference: ", paste(dim(data) - dim(data.filtered), collapse = ", "))
return(data.filtered)
}
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