Hi,

I am trying to learn how to use SVA. The example given in the bioconductor
manual runs pretty smoothly with 1000 genes and 20 samples (10 in each
group, genes 1-300 respond to the primary variable of interest, genes
200-500 respond to some other variable). However, if I change the matrix to
either the following two scenarios:
A. There is no difference between the two groups. svadata <-
cbind(matrix(rnorm(10000),nc = 10),matrix(rnorm(10000),nc = 10))
B. There is a big difference between the two groups. svadata <-
cbind(matrix(rnorm(10000),nc = 10),matrix(rnorm(10000)+2,nc = 10))

The run returns "No Significant surrogate variables". I am wondering under
what conditions can SVA be applied? Must it be a mixture of both
differentially expressed genes and non differentially expressed genes? The
data at my work has many confounding variables and the p-value distribution
is tailed towards 0, does SVA apply to this scenario?

Thanks a bunch,

-Jack

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