preprocess: Function for processing raw LCMS data

In HTDA: HTDA (High Throughput Data Analysis)

Description

The preprocess function denoise, baseline correct, align, identified peaks and normalize them. The results can be visualize by thr baseline corrected and peak identification plots.

Usage

preprocess(file, phenoData, method = "loess", cutoff = 100, plot = TRUE, ...)

Arguments

file	name and location of the folder having raw LCMS data
phenoData	text file contains name and condition of each file
method
cutoff
plot
...

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (file, phenoData, method = "loess", cutoff = 100, plot = TRUE, 
    ...) 
{
    library(PROcess)
    if (plot == TRUE) {
        pdf("BaselineCorrection.pdf")
        bscorr <- rmBaseline(file, method = method)
        dev.off()
        normSpect <- PROcess::renorm(bscorr, cutoff = cutoff)
        t <- as.matrix(rowMeans(normSpect))
        tt <- cbind(rownames(t), t[, 1])
        mode(tt) <- "double"
        pdf("PeakIdentified.pdf")
        Peaks <- PROcess::isPeak(tt[tt[, 1] > cutoff, ], plot = plot, 
            zerothrsh = 1, ratio = 0.1)
        dev.off()
        grandpvec <- round(Peaks[Peaks$peak, "mz"])
        Quanti <- PROcess::getPeaks2(normSpect, grandpvec)
        pData <- data.frame(phenoData)
        new("NcmsProcessData", qData = Quanti, phenoData = pData, 
            BsData = bscorr, normSpectra = normSpect)
    }
  }

HTDA documentation built on May 2, 2019, 4:53 p.m.