When I using DESeq2, I encounter a hurdle.

The gene I knocked down has a global influence on mRNA degradation. According to previous studies base on microarray, about 15% of all gene was up-regulated after it was knocked down. So, I think the default parameters DESeq2 used maybe not suitable.

I think if DESeq2 could normalized counts base on that most genes were not up-regulated or down-regulated after knocked down, it may got reasonable results. Because when I used default parameters, the gene I knocked down was not change, but internal reference genes used in routine experiments were up-regulated.

Although spike-in sequences may be more appropriate for my data normalization, I didn't spike-in when sequencing. Could you give me some suggestions?

Thanks!

Best regards,

If you can identify the genes you want to use for estimating size factors you can pass them as controlGenes, but we don’t have a way to detect these. It’s not an identifiable set without prior knowledge of the experiments.