5.7 years ago by
Please keep in mind that one of the key limitations to any enrichment analysis is that it assumes the variables are independent, but we know that genes are highly dependent on each other in various systems. So you will likely get a number of false positives using any kind of gene set enrichment.
We offer a tool called iPathwayGuide, that will "almost" do what you are looking to do. We still require you to upload two sets of data. Soon, however, we will offer the ability to process publicly available data (e.g. from NCBI-GEO) and then input your list of genes to understand what systems given that phenotype comparison are those genes of interest implicated. That new capability should be out soon.
For now, however, what you can do is find a representative public data set, run it through GEO2R, upload the resulting differential expression data into iPathwayGuide, then reprocess the same data, but artificially make your genes of interest DE by giving them a significant p-value (e.g. 0.01) and all others, an insignificant p-value (e.g. 0.5). The key, is you want to preserve the logFC. The reason for this is one of the key analyses we perform is a perturbation analysis. iPathwayGuide will take the gene expression for your target genes and propagate that perturbation downstream. From this we can identify which pathways are most perturbed. This method virtually eliminates false positives. Then you can compare the two data sets using our meta analysis. This will confirm where any overlap occurs.
Here's a screenshot of the meta analysis for pathways comparing three datasets.