Multiple groups and two methods to analyse them with DESeq2
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Mozart ▴ 20
@mozart-20625
Last seen 22 months ago

Dear Michael, when it comes to analyse different comparisons in a complex experimental design, generally speaking, there are two apparent solutions:

1. first one is to consider the whole experiment (i.e. using just one Sample table containing all the samples) and write a string of code to select desired comparisons or,
2. secondly, select desired group and run the analysis, separately (i.e. duplicating the folder containing just the desired list of samples).

Let's do an example: let's say we have three conditions (wt, ko, wt-treated); then, I want to know the differentially expressed genes coming from wt-treated vs wt condition and ko vs wt condition. If I am following point 1 above, then, I can do the following contrast and get results:

dds$condition <- relevel(dds$condition, ref = "wt")


If I am using the same string of code but, I merely duplicate my folder with aligned files (e.g. just wt and wt-treated) together with another sample table with just the selected comparisons, I may ask myself if I could get differences or not. In that case, I may wondering what's the rationale of choosing one method vs another (regardless the fact the first one is more convenient).

Similar story when we have to calculate PCA plot. If I am subsetting from the whole experiment or if I am picking up just desired samples there are tiny differences in term of maths 'only', I guess. The PCA plots, in other words, look very similar. So, again, given the fact in a certain plot I just want to know the difference between two groups probably method in point number 2 may be better, this time?

deseq2 • 324 views
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@mikelove
Last seen 3 days ago
United States

This is one of the FAQ in the vignette. Could you check there first?