Enrichment Analysis with WebGestalt

Required file

WebGestalt (WEB-based GEne SeT AnaLysis Toolkit) is a free online tool which can perform numerous functions, including pathway or gene set enrichment analysis. The current iteration of this package uses WebGestalt to perform the enrichment analysis. Since this example is using proteomic data to prioritize transcriptomic data, the first step is to evaluate the proteomic data. This analysis is beyond the scope of this wiki, but the end product is a list of protein identifications with BH adjusted p-values. Using a 5% FDR cutoff, this results in 437 proteins (which are entrez gene IDs for this example) and can be found here.

Loading protein list

First, select the organism, which is

hspaiens

Then select the gene ID type, which is

hsapiens_entrezgene_protein-coding

(note: entrez gene IDs were used for this analysis since they seem to be a lowest common denominator when aligning identifications between different analytical pipelines (ex. gene symbols and UniProt accession numbers)

Next, upload the list either as a text file, or by copy and pasting the identifiers into the window. Then hit enter.

Choosing the enrichment analysis

There are many enrichment analysis choices, but for this example, we are using:

KEGG Analysis
WikiPathways Analysis
Transcription Factor Target Analysis

Select one of these choices by mousing over the Enrichment Analysis box (note: it is not required to use all three, and the user should be aware of the rationale behind using each one).

Next a reference set must be specified to enrich against. For this example which uses proteomic data we will specify

hsapiens__entrezgene_protein-coding

For additional parameters, specify:

Statistical Method: Hypergeometric statistical method  
Multiple Test Adjustment: BH
Significance Level: 0.05
Minimum Number of Genes for a Category: 2

Then hit Run Enrichment Analysis.

Downloading results

A new window will have opened with three options. For our purposes we want to Export TSV Only. We can then move this file to our local working directory and rename as the following, depending on the test run.

kegg_protein_enrichment.tsv
wiki_protein_enrichment.tsv
tf_protein_enrichment.tsv

The following steps will guide you through using these files to prioritize a transcriptomic dataset:
KEGG
Wiki
TF

Side note about WebGestalt

Once the data has been loaded, there will be information about how many IDs are successfully mapped. In this example, the following is displayed:

User Data: textAreaUpload.txt. Total number of User IDs: 437. 434 user IDs can unambiguously map to 434 unique Entrez Gene IDs. 3 user IDs were mapped to multiple Entrez Gene IDs or could not be mapped to any Entrez Gene ID. The Enrichment Analysis and GO Slim Classification will be based upon the 434 unique Entrez Gene IDs.

This indicates that all but three protein IDs were successfully mapped. Further information about this mapping can be found in the User ID information table on this main screen, which will provide detailed cross-referenced information about the input, as well as indicate what IDs were not mapped. This can be a useful troubleshooting tool.