RUVNaiveRidge: Removal of unwanted variation for gene correlations.

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

Description

RUVNaiveRidge applies the ridged version of global removal of unwanted variation to simulated or real gene expression data.

Usage

RUVNaiveRidge(Y, center = TRUE, nc_index, nu, kW, check.input = FALSE)

## Default S3 method:
RUVNaiveRidge(Y, center = TRUE, nc_index, nu, kW, check.input = FALSE)

## S3 method for class 'simulateGEdata'
RUVNaiveRidge(Y, center = TRUE, nc_index, nu, kW, check.input = FALSE)

Arguments

Y	A matrix of gene expression values or an object of class simulateGEdata.
center	A logical scalar; if TRUE the data is centered, if FALSE data is assumed to be already centered.
nc_index	A vector of indices of negative controls.
nu	A numeric scalar value of nu ≥q 0.
kW	An integer setting the number of dimensions for the estimated noise.
check.input	A logical scalar; if TRUE all input is checked (not advisable for large simulations).

Details

The parameter kW controls how much noise is cleaned, whereas the parameter nu controls the amount of ridging to deal with possible dependence of the noise and the factor of interest.

Value

RUVNaiveRidge returns a matrix of the cleaned (RUV-treated) centered gene expression values.

Author(s)

Saskia Freytag, Laurent Jacob

References

Jacob L., Gagnon-Bartsch J., Speed T. Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed. Berkley Technical Reports (2012).

Examples

Y<-simulateGEdata(500, 500, 10, 2, 5, g=NULL, Sigma.eps=0.1, 
250, 100, intercept=TRUE, check.input=FALSE)
Y
Y.hat<-RUVNaiveRidge(Y, center=TRUE, nc_index=251:500, 0, 9, check.input=TRUE)
cor(Y.hat[,1:5])
Y$Sigma[1:5,1:5]
Y.hat<-RUVNaiveRidge(Y, center=FALSE, nc_index=251:500, 0, 10, check.input=TRUE)
cor(Y.hat[,1:5])
Y$Sigma[1:5,1:5]

RUVcorr documentation built on Nov. 8, 2020, 5:10 p.m.