Hi,

I have a big rnaseq data set (146 samples), I did the voom transformation with a design matrix that have my factor with my 4 groups of interest and 4 more factors that could affect the expression as well, but I am not interested in them,only added to catch the variation they may introduce.

I did a PCA of the weights that voom returns, and I saw my samples clustered in the 4 groups I am interested to do the DE, so suddenly I had the question what this means? Is something that we should expect, or that means that are some bias, or is not important?

I tried to think about it, I thought that weight won't be correlated with anything, but some genes are doing the separation of the samples because of the weight values. Since weights are used in the glm, and they are correlated with my groups, don't know if results will be correct.

thanks in advance

I am not sure what you hope to learn from doing a PCA of the voom weights.

The voom weights are higher for larger counts. If some of your samples have higher sequencing depths than other samples, then the weights be will be systematically higher for those samples. If the samples in one of your groups or factors have higher than average sequencing depths then the voom weights will also reflect that distinction.

In general, the voom weights may well be associated with factors in your experiment because they are a function of the fitted count sizes, however this doesn't introduce bias. The weights just make the DE analysis more powerful by reflecting the variance trend that larger counts have smaller coefficients of variation.

I don't think that different groups having different weights is necessarily a concern. That might just indicate that some groups are more variable than others.