I am very new to the bioinformatics space and am still trying to figure out how to make sense of my data. Thus far, I have used
DESeq2 to create results and matrices with respect to RNA-Seq data.
I have information about the genes in my dataset, their log2Fold change, L2F standard error, pvalue, p-adjusted value, and type of selection (Positive and Negative). I have made visualizations via heat maps (both normalized for count #, relative distances), tables, and volcano plots based on the above information. The data is from human cell lines.
I now want to get a better sense of the pathways that are enriched given the differential gene expression. I figured that I would start with
PantherDB since it seems to be the easiest means of loading the data.
With that being said, for
- Should I provide all of the genes with differential expression? Or just those with either positive or negative selection (and evaluate the two separately)? I gather the former but figured I would ask.
- Would the list of genes alone be sufficient? Or should I provide any other quantitative/numeric information (e.g. Log2Fold change) to enable the program to better weight the genes/pathways? Based on the paper, it seems like the
Statistical Enrichment testrequires a "numerical value" but I do not know if log2Fold change alone (without p-value) would be sufficient.
- Given that I want to see specific pathways, what would be the best analysis to conduct? I would imagine one of the statistical enrichment tests would be best but I wanted to check. I imagine each test would provide valuable insights - is there anything I should do to ensure I can best understand what I am looking at if I look at multiple tests?
- This is a novice question but, since I have a large gene set, I seem to need an organism from the drop-down menu. Is there nothing for homo sapiens?
I also would like to better acquaint myself with some other means to look at GSEA. Does anyone have recommendations for potential tools other than
Thank you for your patience, \ NE