Entering edit mode
4.7 years ago
jonathanpa12
▴
10
Hello, everyone
I am analyzing a datasets of RNA-seq from different stages of cervical cancer. I would like to identify up or down-regulated immune related genes between the different stage. I have a list of the immune related genes and I would like to filter these genes before the differential expression analysis, using Deseq2, but I don't know if this could be a correct method to achieve my objective. Any help or suggestion? Thank you so much.
Jonathan Pena
Why do you want to filter? Is the reason that those immune-related genes do not show up in the final list of differentially expressed genes? Or do you have a very strong biological rationale that the immune-related genes should be the ones driving the differences between the different conditions that you're analyzing?
The final list of differentially of expressed gene shows only two immune-related gene. I was thinking that those genes usually have very low expression rates but biologically minimal differences in gene expression sometimes may produce significant changes in immune response, Because of this I thought maybe filtering them before differential expression analysis could be an option, but I don’t have a lot of experience in this type of analysis and I don’t know if it would be right to do it.
When you say "final list of differentially of expressed gene shows only two immune-related gene", do you mean they are not DE, or they are NA?
Sorry, I must learn to ask better. I used Deseq2 to make the differential expression analysis and I got 11 genes using as significance thresholds: Adjusted p-value: 0.05 and Log2 fold change: 1. Only one of the 11 genes is a immne-related gene of the list that I created manually.
So, you are saying you have a total of 11 DE genes of which 1 overlaps with a hand-picked list of genes of interest?
Exactly. And I would like to validate experimentally this analysis later, so I think if I can get more genes would be better.
That doesn't sound as if there's a lot to be done here. 11 DEG is not a whole lot to begin with, which makes me think that these samples aren't really that different (or there's a lot of variability between the replicates). You can always check the p-value (not the adjusted p-value) of the genes of interest, which will give you some insights into whether these immuno-related genes show any promise in these samples. After all, pre-filtering mostly influences the severity with which the "raw" p-values are adjusted as Ian has pointed out in his answer below.
Thank you so much for your help