Functional enrichment / Enrichment with your own annotation
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Your genes of interest (e.g. differentially expressed genes) are compared with each gene set: a set is enriched when it contains more of your genes than expected by chance. Gene sets can come from a GMT file or from your own gene–term annotation table (GO, pathways or custom categories).
The background matters: supply every gene actually measured in the experiment; otherwise genes that could never have been selected inflate significance. By default the background is the annotated genes of your background list, as in widely used enrichment software; you can switch to the entire background list.
In the table, k = overlapping genes, n = genes of interest tested, K = set size within the background and N = background size; gene ratio = k/n and fold enrichment = (k/n)/(K/N). Only sets that overlap your list enter the multiple-testing correction.
Significant enrichment means statistical over-representation only, not activation or repression of a pathway; judge direction from up- and down-regulated genes (for example with the z-score enrichment bubble plot).
One-sided hypergeometric test P = P(X ≥ k), identical to scipy.stats.hypergeom.sf(k − 1, N, K, n) and the one-sided Fisher exact test; Benjamini & Hochberg (1995) J R Stat Soc B 57:289–300. Example gene sets: Liberzon A. et al. (2015) Cell Systems 1:417–425.
Up to 500,000 annotation rows.
Send us your data and research question and you will receive a written plan within 1 working day: analysis steps, parameter rationale, deliverables and timeline. Quoted per project.