Hey everybody!
I would like to hear your opinion about using pseudocounts when performing RNA-seq analysis. I have always been following the standard protocol for DESeq2 when analyzing RNA-seq data, using their great guide, but recently a colleague pointed out that I should always add the value 1 to counts of all genes. He mentioned that this will help for those genes which have a count of 0, in order to avoid dealing with negative values after transformation.
I always filter out low counts genes with:
filtered <- rowSums(counts(dds)) >= 10
So I wonder if DESeq2 already deals with pseudocounts by default or if it's required that I perform this step myself.
Thanks!
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written
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Marco Pannone •
Pseudocounts are commonly added before log-transformation of normalized counts for downstream analysis, to avoid taking logs of zeros and logs of values > 0 < 1 which as you say would be neagtive. For the DE testing you should not do that. Give it the raw counts as clearly described in the manual.
Thanks you for your answer! So for example I should use pseudo counts when plotting heatmaps, volcano plots, PCA plots, etc.? And when it comes to GSEA, where I use as input the normalised read counts matrix for all genes and replicates, should I also use pseudocounts in that case?
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