empNegativeControls: Empirically choose negative control genes.

In PeteHaitch/RUVcorr: Removal of unwanted variation for gene-gene correlations and related analysis.

Description

empNegativeControls finds suitable negative controls in real or simulated data.

empNegativeControls.default empirically chooses negative control genes for matrix input.

empNegativeControls.simulateGEdata empircially chooses negative control genes for simulateGEdata object.

Usage

empNegativeControls(Y, exclude, smoothing = 0.1, nc)

## Default S3 method:
empNegativeControls(Y, exclude, smoothing = 0.1, nc)

## S3 method for class 'simulateGEdata'
empNegativeControls(Y, exclude, smoothing = 0.1, nc)

Arguments

Y	A matrix of gene expression values or an object of the class simulateGEdata.
exclude	A vector of indices to be excluded from being chosen as negative controls.
smoothing	A numerical scalar determining the amount of smoothing to be applied.
nc	An integer setting the number of negative controls.

Details

First the mean of all genes (except the excluded genes) is calculated and genes are accordingly assigned to bins. The bins have the size of the smoothing parameter. In each bin the function picks a number of negative control genes proportional to the total number of genes in the bin. The picked genes in each bin have the lowest inter-quantile ranges of all genes in the respective bin.

Value

empNegativeControls returns a vector of indicies of empirically chosen negative controls.

Warning

For simulated data it is advisable to use the known negative controls or restrict the empirical choice to the known negative controls by excluding all other genes.

Author(s)

Saskia Freytag

Examples

Y<-simulateGEdata(500, 500, 10, 2, 5, g=NULL, Sigma.eps=0.1, 250, 100, check.input=TRUE)
empNegativeControls(Y, exclude=1:100, nc=100)

PeteHaitch/RUVcorr documentation built on May 8, 2019, 1:31 a.m.