Nothing
quantile_normalisation <- function(df) {
df_rank <- apply(df, 2, rank, ties.method = "min")
df_sorted <- data.frame(apply(df, 2, sort))
df_mean <- apply(df_sorted, 1, mean)
index_to_mean <- function(my_index, my_mean) {
return(my_mean[my_index])
}
df_final <- apply(df_rank, 2, index_to_mean, my_mean = df_mean)
rownames(df_final) <- rownames(df)
return(df_final)
}
#' rc.feature.normalize.quantile
#'
#' normalize data using quantile
#'
#' @param ramclustObj ramclustObj containing MSdata with optional MSMSdata (MSe, DIA, idMSMS)
#' @return ramclustR object with normalized data.
#' @export
rc.feature.normalize.quantile <- function(ramclustObj = NULL) {
ramclustObj$MSdata <- t(quantile_normalisation(t(ramclustObj$MSdata)))
if (!is.null(ramclustObj$MSMSdata)) {
ramclustObj$MSMSdata <- t(quantile_normalisation(t(ramclustObj$MSMSdata)))
}
## update msint and optionally msmsint
global.min <- apply(cbind(ramclustObj$MSdata, ramclustObj$MSMSdata), 2, "min", na.rm = TRUE)
ramclustObj$msint <- compute_wt_mean(
ramclustObj$MSdata,
global.min,
ramclustObj$fmz,
TRUE
)
if (!is.null(ramclustObj$MSMSdata)) {
ramclustObj$msmsint <- compute_wt_mean(
ramclustObj$MSMSdata,
global.min,
ramclustObj$fmz,
TRUE
)
}
ramclustObj$history$normalize.quantile <- paste(
" Features were normalized using 'quantile' normalization."
)
return(ramclustObj)
}
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