Thank you very much. I have just received a pathway enrichment analysis for the first time and was overwhelmed. Your explanations clarified much of the puzzle.
I am studying the analysis of transcriptomics and metabolomics. I like these videos so much about enrichment analysis, which are very helpful. lf you don't mind, l would like to ask your permission to share this video to the other website in China for the embarrassing reason that RU-vid is blocked from accessing in China. Of course, l will give sources of the original website. Thank you very much.
@@biostatsquid I am sorry, it seems that uploading the link is not permitted. I will send the website to you by private message. Thank you for your understanding.
Your videos are great and very easy to follow. For the background genes, how do you download GSEA GMT files for only genes expressed in the specific tissue you are interested in.
Thanks so much for your feedback! Hmm as far as I know, you cannot do that. But you can download the full .gmt file and then just filter it for all of the genes you detected in your tissue.
Hi, it was the best video I have ever seen regarding PEA but mam how about if we wanna perform PEA on pangenome data..is it possible to do that directly by just providing genome seq
can we put non-differential genes identified in our analysis in the background list? or we have to put the whole human genes in that? because in contingency table it is showing non-differentially expresed (for Fisher's exact test calculation). please confirm
Hi Prabhakar, thank you for your question. Your background list should contain genes that CAN be measured in your experiment. Also non-differentially expressed genes, since they were still detected and expressed by your cells (just not differentially expressed between conditions). As I mention in the video, if you are, for example, studying gene expression in liver cells, and you use as a background gene set ALL human genes, PEA will probably tell you that your gene set is enriched for liver-pathway related genes. That is true, but not very helpful. So you want to use a background list tailored to your experiment: include all genes that CAN be measured in your experiment (including general genes such as cell cycle etc, but also liver-specific genes, while excluding genes that are specific to other cell types, for example). So in summary, your background list would include all the genes that you were able to measure (not just only DEGs from your downstream analysis). Hopefully this helped:)
