meanCorOl {ncGTW} | R Documentation

## Compute average pairwise correlation and overlapping area

### Description

This function computes average pairwise correlation and overlapping area of each sample pair.

### Usage

    meanCorOl(ncGTWinput, sampleRt)

### Arguments

`ncGTWinput` | A list in which each element is a `ncGTWinput` object.
`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.

### Details

This function computes the pairwise correlation and overlapping area of each sample pair from the input feature, and then takes average.

### Value

A list in which the first element is average pairwise correlation, and the second one is average overlapping area.

### Examples

    # 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
    }

[Package ncGTW version 1.0.0 Index]