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Hi,

We use a local Galaxy to run PCA (principal component analysis) based on six mouse RNA-Seq data. However, our result is weird: (1) PC1 can explain nearly all variation (94.5%); (2) All six samples on the 0.4 of PC1. Could you help us? Many thanks.

Best,

Gary

If almost all the variance is explained by the first PC, it means that the variables are collinear, i.e. they can all be expressed as a linear transformation of one of them. If this is not what you expect, check that the data is really what it should be.

This is from plotPCA in deepTools, which unfortunately defaults to not transposing the matrix before computing the PCA (I assume it was done this way originally since the PCA() function in matplotlib doesn't accept matrices with more columns than rows). So in this case the results just indicate that "genes are quite variable, but similar between samples", which is OK for basic QC but usually not what people actually care to look at in a PCA.

Many thanks to your super professional answer.