vooma(y, design=NULL, correlation, block=NULL, plot=FALSE, span=NULL) voomaByGroup(y, group, design=NULL, correlation, block=NULL, plot=FALSE, span=NULL, col=NULL, lwd=1, alpha=0.5, pch=16, cex=0.3, legend="topright")
EListobject, or any similar object containing expression data that can be coerced to a matrix.
logicalvalue indicating whether a plot of mean-variance trend should be displayed.
0for fully transparant to
1for fully opaque.
voomais an acronym for mean-variance modelling at the observational level for arrays.
vooma estimates the mean-variance relationship in the data, and uses this to compute appropriate weights for each observation.
This done by estimating a mean-variance trend, then interpolating this trend to obtain a precision weight (inverse variance) for each observation.
The weights can then used by other functions such as
lmFit to adjust for heteroscedasticity.
voomaByGroup estimates precision weights separately for each group. In other words, it allows for different mean-variance curves in different groups.