shiftCor: shiftCor for GUI
In 13479776/statTarget: Statistical Analysis of Molecular Profiles

Description Usage Arguments Value Examples

View source: R/shiftCor_v2.R

shiftCor provides the QC based signal correction for large scale metabolomics and targeted proteomics.

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shiftCor(samPeno, samFile, Frule = 0.8, MLmethod = "QCRFSC",
  ntree = 500, QCspan = 0, degree = 2, imputeM = "KNN",
  coCV = 30, plot = FALSE)

`samPeno`	The file with the meta information including the sample name, batches, class and order.
`samFile`	The file with the expression information.
`Frule`	Modified n precent rule function. A variable will be kept if it has a non-zero value for at least n precent of samples in any one group. (Default: 0.8)
`MLmethod`	The machine learning method for QC based signal correction. i.e. QC based random forest signal correction (QC-RFSC) and QC based LOESS signal correction (QC-RLSC).
`ntree`	Number of trees to grow in random forest model.
`QCspan`	The smoothing parameter for QC-RLSC which controls the bias-variance tradeoff in QC-RLSC method if the QCspan is set at '0', the generalised cross-validation will be performed to avoid overfitting the observed data.
`degree`	Lets you specify local constant regression (i.e., the Nadaraya-Watson estimator, degree=0), local linear regression (degree=1), or local polynomial fits (degree=2, the default) for QC-RLSC.
`imputeM`	The parameter for imputation method i.e., nearest neighbor averaging, 'KNN'; minimum values, 'min'; Half of minimum values, 'minHalf'; median values, 'median'.
`coCV`	Define the cutoff value (0-100) of CV for controlling the number of features.
`plot`	Defines if images of feature quality should be generated (TRUE) or not (FALSE). Defaults to FALSE.

the shiftCor files. See the details at https://stattarget.github.io

datpath <- system.file('extdata',package = 'statTarget')
samPeno <- paste(datpath,'MTBLS79_sampleList.csv', sep='/')
samFile <- paste(datpath,'MTBLS79.csv', sep='/')
shiftCor(samPeno,samFile, MLmethod = 'QCRFSC', imputeM = 'KNN',coCV = 30)