Hello,
I'm a master student in a biological-related field and I'm using DESeq2 for differential gene expression analysis; when I evaluate the result of the output 3 genes results as very down-regulated, however if I look at the read counts there are 4 reads in just one of the 8 cells of untreated samples and no reads in the 26 cells of the treated samples. A similar thing happens for the other two genes. Reading the guide that you provide I found that no pre-filtering of the read counts is needed rather is better to give the reads as they are obtained to DESeq2; however it seems that the software has a bias due to the abovementioned results. I have not further investigated the problem, I just report it.
Data that I used are freely available on CommonMind Consortium: https://www.synapse.org/#!Synapse:syn11617751, related metadata are https://www.synapse.org/#!Synapse:syn11638462; as model I used Dx (treatment received by monkeys) and as covariates Sex and DLPFC_RNA_isolation_Batch as reported in https://www.ncbi.nlm.nih.gov/pubmed/27668389 (online methods related to monkeys).
Regards,
Francesco Biagi
Hello MIcheal,
sorry for the delay, yes they are all significant. Yes, I have already fixed the problem, this post was ment in order to make aware mantainers of that problem, because as they said in their manual this pre-filtering step should not be needed at all.. as you said: "..filtering is not a requirement for the method to work."
However thank you a lot for you help.
Best Regards,
Francesco
Thanks for the post. Could you also say what version of DESeq2 you use? Are you using a Wald test (the default)? Just simple two group comparison? I wouldn’t think that these would have very small pvalues at all.
Is this similar to the setup you describe? I'm trying to figure out what range of fitted parameters would give a small p-value for such a gene.
dds <- makeExampleDESeqDataSet(m=34) dds$condition <- factor(rep(1:2,c(8,26))) counts(dds)[1,] <- rep(c(4L,0L),c(1,33)) > counts(dds)[1,] sample1 sample2 sample3 sample4 sample5 sample6 sample7 sample8 sample9 sample10 sample11 4 0 0 0 0 0 0 0 0 0 0 sample12 sample13 sample14 sample15 sample16 sample17 sample18 sample19 sample20 sample21 sample22 0 0 0 0 0 0 0 0 0 0 0 sample23 sample24 sample25 sample26 sample27 sample28 sample29 sample30 sample31 sample32 sample33 0 0 0 0 0 0 0 0 0 0 0 sample34 0 dds <- DESeq(dds) res <- results(dds) > res[1,] log2 fold change (MLE): condition 2 vs 1 Wald test p-value: condition 2 vs 1 DataFrame with 1 row and 6 columns baseMean log2FoldChange lfcSE stat pvalue <numeric> <numeric> <numeric> <numeric> <numeric> gene1 0.109431114293273 -1.42793623765064 3.49906906619771 -0.408090326494275 0.683207361697624 padj <numeric> gene1 0.975948164280249 > packageVersion("DESeq2") [1] ‘1.20.0’