preprocess {plsgenomics} | R Documentation

## preprocess for microarray data

### Description

The function `preprocess` performs a preprocessing of microarray data.

### Usage

    preprocess(Xtrain, Xtest=NULL,Threshold=c(100,16000),Filtering=c(5,500),log10.scale=TRUE,row.stand=TRUE)

### Arguments

`Xtrain` | a (ntrain x p) data matrix of predictors. `Xtrain` must be a matrix. Each row corresponds to an observation and each column to a predictor variable.
`Xtest` | a (ntest x p) matrix containing the predictors for the test data set. `Xtest` may also be a vector of length p (corresponding to only one test observation).
`Threshold` | a vector of length 2 containing the values (threshmin,threshmax) for thresholding data in preprocess. Data is thresholded to value threshmin and ceiled to value threshmax. If `Threshold` is NULL then no thresholding is done. By default, if the value given for `Threshold` is not valid, no thresholding is done.
`Filtering` | a vector of length 2 containing the values (FiltMin,FiltMax) for filtering genes in preprocess. Genes with max/min$<=q FiltMin$ and (max-min)$<=q FiltMax$ are excluded. If `Filtering` is NULL then no thresholding is done. By default, if the value given for `Filtering` is not valid, no filtering is done.
`log10.scale` | a logical value equal to TRUE if a log10-transformation has to be done.
`row.stand` | a logical value equal to TRUE if a standardisation in row has to be done.

### Details

The pre-processing steps recommended by Dudoit et al. (2002) are performed. The default values are those adapted for Colon data.

### Value

A list with the following components:

`pXtrain` | the (ntrain x p') matrix containing the preprocessed train data.
`pXtest` | the (ntest x p') matrix containing the preprocessed test data.

### Author(s)

Sophie Lambert-Lacroix (http://www-lmc.imag.fr/lmc-sms/Sophie.Lambert) and Julie Peyre (http://www-lmc.imag.fr/lmc-sms/Julie.Peyre/).

### References

Dudoit, S. and Fridlyand, J. and Speed, T. (2002). Comparison of discrimination methods for the classification of tumors using gene expression data, Journal of the American Statistical Association, 97, 77–87.

### Examples

    # load plsgenomics library
    library(plsgenomics)

    # load Colon data
    data(Colon)
    IndexLearn <- c(sample(which(Colon$Y==2),27),sample(which(Colon$Y==1),14))

    Xtrain <- Colon$X[IndexLearn,]
    Ytrain <- Colon$Y[IndexLearn]
    Xtest <- Colon$X[-IndexLearn,]

    # preprocess data
    resP <- preprocess(Xtrain= Xtrain, Xtest=Xtest,Threshold = c(100,16000),Filtering=c(5,500),log10.scale=TRUE,row.stand=TRUE)

    # how many genes after preprocess ?
    dim(resP$pXtrain)[2]

[Package plsgenomics version 1.2 Index]