RUVNaiveRidge {RUVcorr} | R Documentation |
RUVNaiveRidge
applies the ridged version of global removal of unwanted variation
to simulated or real gene expression data.
RUVNaiveRidge.default
applies the ridged version of global
removal of unwanted variation to matrices.
RUVNaiveRidge.simulateGEdata
applies the ridged version of
removal of unwanted variation to objects of class simulateGEdata
.
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)
Y |
A matrix of gene expression values or an object of
class |
center |
A logical scalar; if |
nc_index |
A vector of indices of negative controls. |
nu |
A numeric scalar value of |
kW |
An integer setting the number of dimensions for the estimated noise. |
check.input |
A logical scalar; if |
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.
RUVNaiveRidge
returns a matrix of the cleaned
(RUV-treated) centered gene expression values.
Saskia Freytag, Laurent Jacob
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).
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]