I'm working with DESeq2 to make a DE analysis between samples in two different conditions. During the analysis, I identified a batch effect due to the sequencing time modelled as a covariate in the design formula. From the differential expression (Wald test), I was able to retrieve a good number of significant genes (~100) but the LogFC range looks not reliable going from -30 to +30.

• What could be the cause of these extreme large values and how can I solve the problem? I tried to use lfcShrink() to re-estimate the logFC but I'm not sure that is sufficient to achieve reliable results.

• My second question is about the design of the model. Is it reasonable to add covariates to the model also if they don't show a strong effect on the data (looking PCA or clustering)?

Follow the code used for the analysis. Thanks for your help!

dds <- DESeqDataSetFromMatrix(count, coldata, design = ~ Group + Condition)

keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]

dds <- DESeq(dds)

res <- results(dds, contrast=c("Condition","dis","hea"))
res <- res[which(res$padj <0.05),]

Agree with ATpoint, look at the genes with large LFC (from lfcShrink) using plotCounts. Usually large MLE LFC are from all 0's in one group.

"Is it reasonable to add covariates to the model also if they don't show a strong effect on the data"

This is up to the analyst. You can add them to be careful but it does come at a loss of degrees of freedom. So if they truly don't have any effect on any genes, it's best to leave out unnecessary covariates.