I got here the explanations I didn't get from my university teachers, thank you! 1:09:01 I think you can do the same (as the "borderline tacky code") with this one-liner gene_set_list <- split(H$ensembl_gene, H$gs_name)

my genes like cd45 and sox10 are not within the hg19 pbmc data ! what I should do ?

Hi @Kim Dill-McFarland, thanks for this helpful and insightful intro to R tidyverse. Could you please provide the data for dat$E in your presentation for our practice?

Hi, great video and clarification of types of enrichment analyses. I have a question, what is the best way to create a ranked list of genes for 3 treatment and 3 control samples in one data frame using just normalized read counts. I want to rank the gene list from all genes not DEGs then do enrichment analysis. Thank you!

I'm new to this and I'm wondering why do you need to see how much your significant genes overlap with a larger or other gene set? Is that to elucidate what transcriptional regulation network controls the significant genes and or to discover other similar genes relative to the genes of interest?

How to download the genesets directly in R studio?

As a guy who got recommended this video on youtube for some reason, who has no experience in molecular biology, nor anything related to biology or scientific expert fields, let me give you my 2 cents : you have many errors here and there for which you will need to re-analyze from the beginning. I will not pin point what is wrong nor why, just pointing out that in my expert point of view, this whole video was based on false data. Try again BUCKO :D:D:D:D:D:D

can we do the gene set enrichment analysis for rice using the same code and databases

Thank you! Spend a while trying to figure out how to do pathway analysis in R and most guides always expected you already have some sort of GO or Kegg library where you can refer to and don't go into specifics how these libraries work and what to do when they do not work. This step-by-step guide was enough to get me from DEG lists into proper pathway analysis - and I even understood why and what I am doing in each step! I am working with rat sequencing data and some columns I had were very different from the example data you had here but after checking specific points a few times I managed to filter and re-format all the necessary information from my data.

Thankyou! I was just wondering which paper to cite when performing the hypergeometric "simple" enrichment?

I really enjoyed your presentation. I learned quite a bit. Thank you!

Thanks for the tips, dr Kim. I have a question: why does && not work under dplyr context? afaik, only & works

Had to read your paper first 😅

Hi Kim. @6:10 I see WGCNA installation? I'm busy with that analysis and I'm tripping up. Are you doing that analysis as well? Will you make an R tutorial for WGCNA?

Best channel ever, thank for keeping posting

Very clear explanation, thanks for this amazing content! Would you have any additional bio-inf analysis tutorials?

Hi how can we do the gsea analysis for dna methylation genes i have beta values of samples and logFC cutoff of the same, thank you

Great video! I learned about msigdbr and the dplyr::separate function. I just want to mention a few things. 1. The GSEA ranking metric doesn’t have to be fold-change. I use the gene wise average moderated t-statistic from limma or the signed -log10-transformed p-value. There are a ton of ranking metrics to choose from. Both of these are very similar, and we can compare their density plots to get an idea of how they would alter the GSEA results. 2. Over-representation analysis is not great as a follow-up to differential analysis because of the arbitrary significance threshold that you mentioned and the fact that there may be duplicates at the gene level. Also, we lose information about the direction of change, since ORA only tells us which sets are more present in the significant group than what we expect by chance. However, it is great when genes uniquely map to discrete clusters, so it is good as a follow-up to WGCNA or K-means clustering. 3. The figures you use to introduce GSEA show the phenotype permutation approach, but most R implementations (including fgsea) use the gene permutation approach, which is much faster but has a slightly different interpretation. 4. For ORA, it may be useful to plot the ratio of the number of significant genes in the gene sets to the total number of significant genes along the x-axis and change the bars to points scaled according to the -log10(adjusted p-value). Gene sets that include all significant genes (ratio of 1) may be interesting to look at, even if their adjusted p-values are hovering near 0.05. 5. The fora function in fgsea can be used for ORA as well. Personally, I find it easier than dealing with the bulkier clusterProfiler results objects.

HELLO, How can we add gene count and pvalue in same histogram by using clusterprofiler package of R?

Thank you so much this was so helpful!

Thank you for the very clear explanations. One question is that for the purpose of GSEA (either simple or gsea), what type of normalization of the counts should one use? Or does it even matter? If so, how would it be different between the two methods? Thank you!