DESeq2 Shrink cutoff
Hi,
at the moment I am analysing a simple dataset comparing two conditions using DESeq2. For DEG calling i usually go for padjust < 0,05 and foldchage of > 2. I am always wondering whether I should use for that the shrinked fold change or the fold change deseq2 is giving me straight after the analysis.
My code ist pretty straight forward using apeglm for shrinkage. A typical scatterplot I get after the analysis looks like this. I use the following code to extract the counts:
normalized_counts <- data.frame(counts(dds, normalized=TRUE))
Lots of gens which look like DEG in the plot are not due to shrined log2folchange. Is there any issue in my pipeline in your opinion?
Link to Scatterplot: https://ibb.co/HNF4nNZ
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You should plot fold changes e.g. using
plotMAfuncrion rather than this kind of plot.I always use shrinked result. Shrinkage only change foldchange not p value. Mean counts value is easier affected by outlier value.