Gene-set enrichment and Enrichr

Gene-set enrichment and Enrichr#

A table of significant genes is hard to read. Over-representation analysis asks whether those genes pile into known biological programs more than you’d expect by chance.

How the test works (this module)#

  1. Build a list of “interesting” genes (here: FDR < 0.05 and |log2FC| > 1).

  2. Compare that list to a library of curated gene sets.

  3. For each set, ask whether the overlap is larger than expected under a hypergeometric (or Fisher) model, then adjust p-values across sets.

That is what gseapy.enrichr does: it sends your HGNC gene symbols to the Enrichr server and returns overlap statistics and adjusted p-values. gseapy is the Python client you call from Colab.

MSigDB#

The Molecular Signatures Database (MSigDB) is a curated catalog of gene sets used across transcriptomics. Each set is a named list of genes that prior work associated with a process, pathway, or transcriptional program. Enrichr hosts many MSigDB collections as libraries you can query by name.

Hallmark gene sets#

This module uses the Enrichr library MSigDB_Hallmark_2020: the MSigDB Hallmark collection (about fifty sets).

Hallmark sets were built to reduce redundancy in older MSigDB collections. Liberzon and colleagues refined overlapping pathway and process signatures into a smaller panel of coherent programs (for example interferon response, cell cycle, inflammatory signaling). Each Hallmark set keeps genes that consistently cohere across related source signatures, so a hit is easier to narrate than a long list of near-duplicate pathway names. Browse the individual sets and gene membership on the Hallmark collection page.

What Enrichr is and is not#

Is: a fast way to attach Hallmark themes to your differential-expression list.

Is not: proof that a program is active in every cell, or that every gene in the set moved for the same reason. Symbols must match the library (HGNC). Entrez IDs will silently drop.

Practical notes for Colab#

  • Install once: pip install gseapy==1.3.2

  • Call gseapy.enrichr with gene_sets='MSigDB_Hallmark_2020', organism='Human', and outdir=None (unless you want Enrichr to write a folder into the runtime)

  • Needs network access

  • After Enrichr returns, pick one significant set and look at those genes’ expression across samples—that is the lab’s second figure

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