Fast gene set enrichment analysis
Table files (with .txt or .tsv file extension) either from:
- DESeq2 output, or
- LIMMA output, or
- A tab-delimited file with two columns: gene identifiers + and a numeric column, for example
gene_symbol t_statistics TP53 3.43 MUC16 4.59 TTN 1.02 CSMD3 -0.23 ...
- Gene identifiers can be either ENSG, Affy probeset ID, Entrez ID, or HNSC gene symbols.
- If the table is derived from differential expession analysis, the gene list should be a full list (i.e. including both significant and non-significant genes)
In output folder, for each input gene list, a corresponding subfolder will be created with following files
- toplist_fgsea.tsv: A table with pathway enrichment scores and additional statistics
- enrichment_scores.json: A json file containing data for enrichment plots
`-- output |-- gordon_gse71203 |-- enrichment_scores.json `-- toplist_fgsea.tsv
- collection — Name of pathways or gene sets collection, e.g. “H: hallmark gene sets”, “C2: curated gene sets”, “C5: gene ontology (GO)”
- number_of_pathways — Max number of top pathways to show (with p.value <0.05), default: 20
- collapse_pathways — Whether to collpase related pathways into independent ones, default: true