Hi

I'm having some real problems with a miRNA analysis.

I have 36 samples from 4 groups. The four groups share features (9 YA, 9 YB, 9XA, 9XB).

I want to look at differential expression between Y vs X, A vs B, YA vs YB, & XA vs XB

I'm using deseq2 for differential analysis:

dds <- DESeqDataSetFromMatrix(
  countData = reads,
  colData = coldata,
  design= ~0 + condition)

To obtain my results I'm using the following

res_YX<-results(dds, contrast = list(c("conditionYA", "conditionYB"), 
                                        c("conditionXA", "conditionXB")), listValues = c(1,-1), alpha=0.05)

res_AB<-results(dds, contrast = list(c("conditionYA", "conditionXA"), 
                                        c("conditionYB", "conditionXB")), listValues = c(1,-1), alpha=0.05)

res_YAYB<-results(dds, contrast = list(c("conditionYA"), 
                                        c("conditionYB")), listValues = c(1,-1), alpha=0.05)

res_XAXB<-results(dds, contrast = list(c("conditionXA"), 
                                        c("conditionXB")), listValues = c(1,-1), alpha=0.05)

When I put together a PCA plot there really isn't any grouping according to the condition, even I look at the larger groups (Y vs X, A vs B). I have looked at other principle components and excluded potential outliers and it doesn't really help.

Are there any good resources to troubleshoot this or advice about how to proceed?

Thanks

PJC

Agree with the above comment. This is what the PCA is for, it’s an exploratory plot that you don’t need to adjust.