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Discrepancy in DESeq2 fold change

Hello, I am currently analysing RNA-Seq data obtained from Ion Proton. I have triplicates of all my samples, for an empty vector, a WT protein, and 3 mutants. I am having trouble understanding the DESeq2 results. When I compare my WT and my empty vector, in the raw counts I have gigantic differences for my overexpressed protein (in the 60 000 range vs an average of 2 for the empty vector). However, after running the DESeq2 pipeline, the log2Fc is only .6, and it is not the highest FoldChange when it definitely should be. Also, I extracted normalized counts from the results and I transformed them using log2 then calculated the mean and sd and did the fold change, which comes to around to 6.7 . I understand it might be considered as an outlier and taken out of the analysis, but how can I force it keep these values? Secondly, when I manually calculate the log2 fold change in the same way (log2 of normalized counts, mean of log2, sd of log2, fold change of log2), I don't get the same results as when running DESeq2. Am I doing something wrong? Which result should I be looking at?

Thank you for your help.

rna-seq r deseq2

1 answer

Deseq2 fold-changes are shrunken. See my earlier post: A: How to recover treated/control count from DESeq2 output

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