Description Usage Arguments Details Value Examples

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

1 | ```
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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | ```
# 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
}
``` |

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