Hey,

I'm running an experiment with two conditions, one is the wildtype and the other is a knock out of a gene. Each sample is from a single mouse and the samples were isolated on different days. Here is an overview

I ran several PCAs because the initially the samples did not cluster ( )

Next, I checked for Sex and Date and Sex + Date as covariates and ran a PCA as well. I used removeBatchEffect from Limma and the transformed counts from DESeq2 and the PCA looked like this:

,,

As one can see, if I correct for Sex PC2 is showing the difference in my condition of interest but the correction for date or date+sex brings this to the PC1.

I also ran sva with the following model:

mod  <- model.matrix(~ GT, colData(ddsrun))
mod0 <- model.matrix(~   1, colData(ddsrun))
svseq <- svaseq(dat, mod, mod0, n.sv = 2)

and the result is

As a side question, the first column cleary show the effect for the isolation date but the second column doesn't correlate with Sex or Date. So I would not use this. Is there any good way how to interpret this? Right now, I would only try to overlap it with already know and likely covariate and not use it! Otherwise I introduce a batch which I don't know what it means???

With this, I would think that the sex doesn't have such a big impact on the data, but the date has. That means I will use  ~ Date +  GT as design formula for DESeq. Additionaly, I did an mds plot of the euclidean distance and it suggest that the data has an higher impact on the data compared to sex too.

Now I was wondering if these steps are in the correct order and my conclusion is correct? I have another experiment with the same set-up but here the GT effect is less and overlayed by Data and/or Sex

Thanks

Mathias

hi,

I usually recommend people to put into the model those terms that they think might affect gene expression, even if only for some genes, and so long as they have an experimental design that allows it. You have nearly perfect confounding of date and sex, so you can pick the one that has a bigger effect in the PCA plots and put that one in. You can't do much more than that due to the confounding. So I'd also use the design you have suggested, ~date + genotype.