meanCorOl {ncGTW} | R Documentation |
This function computes average pairwise correlation and overlapping area of each sample pair.
meanCorOl(ncGTWinput, sampleRt)
ncGTWinput |
A list in which each element is a |
sampleRt |
A list of the same length as the sample number in which each element is a vector corresponding to the sample raw/adjusted RT. |
This function computes the pairwise correlation and overlapping area of each sample pair from the input feature, and then takes average.
A list in which the first element is average pairwise correlation, and the second one is average overlapping area.
# obtain data data('xcmsExamples') xcmsLargeWin <- xcmsExamples$xcmsLargeWin xcmsSmallWin <- xcmsExamples$xcmsSmallWin ppm <- xcmsExamples$ppm # detect misaligned features excluGroups <- misalignDetect(xcmsLargeWin, xcmsSmallWin, ppm) # obtain the paths of the sample files filepath <- system.file("extdata", package = "ncGTW") file <- list.files(filepath, pattern="mzxml", full.names=TRUE) tempInd <- matrix(0, length(file), 1) for (n in seq_along(file)){ tempCha <- file[n] tempLen <- nchar(tempCha) tempInd[n] <- as.numeric(substr(tempCha, regexpr("example", tempCha) + 7, tempLen - 6)) } # sort the paths by data acquisition order file <- file[sort.int(tempInd, index.return = TRUE)$ix] # load the sample profiles ncGTWinputs <- loadProfile(file, excluGroups) XCMSCor <- matrix(0, length(ncGTWinputs), 1) XCMSOl <- matrix(0, length(ncGTWinputs), 1) for (n in seq_along(ncGTWinputs)){ XCMSmean <- meanCorOl(ncGTWinputs[[n]], slot(xcmsLargeWin, 'rt')$corrected) XCMSCor[n] <- XCMSmean$cor XCMSOl[n] <- XCMSmean$ol }