I have RNA-seq dataset from two population and two conditions as recently posted in https://support.bioconductor.org/p/123895/#123921. I've successfully performed the evaluation of difference between two groups in gene expression changes thanks to you.
Then, I have done pathway analysis based on fold change between two conditions using gage with reference to KEGG pathway.
It seems that there is difference between two groups in pathway analysis results (i. e. some pathway is upregulated by condition change in one group, but not changed in the other group). I would like to evaluate which pathway is significantly different between groups.
Does someone knows how to evaluate it with statistically valid method?
You can do either an overrepresentation analysis (ORA) or gene-set enrichment analysis (GSEA). Those two are entirely different but many seems to got it wrong.
In an overrepresentation analysis, the input is the significant genes after differential expression analysis. You can combine both up/downregulated genes in one list, however, I recommend to do the ORA separately for up and downregulated genes. You can use online tools such as gprofiler or Bioconductor package such as goseq. goseq might be a better option because it also correct your results based on gene length bias. But hey, if you want to get the results quickly, there's nothing wrong with using those web-based tools.
The second way is the GSEA. The input for this method is all your genes that have been ranked. You can rank the genes based on the fold change or moderated t values. If you are using DESeq2then I'd recommend to do lfcShrink with apeglm method. For the GSEA itsef, there are so many options out there and numerous benchmarks have been done and I can only say that each method has its own strength and weaknesses. You can also use gprofiler and then tick the Ordered query tick box (right hand side, under Options). Or you can use something like fgsea which is quite simple to use.