Pathway size matters: the influence of pathway granularity on over-representation (enrichment analysis) statistics

Peter D. Karp, Peter E. Midford, Ron Caspi, Arkady Khodursky

Research output: Contribution to journalArticlepeer-review

22 Scopus citations


Background: Enrichment or over-representation analysis is a common method used in bioinformatics studies of transcriptomics, metabolomics, and microbiome datasets. The key idea behind enrichment analysis is: given a set of significantly expressed genes (or metabolites), use that set to infer a smaller set of perturbed biological pathways or processes, in which those genes (or metabolites) play a role. Enrichment computations rely on collections of defined biological pathways and/or processes, which are usually drawn from pathway databases. Although practitioners of enrichment analysis take great care to employ statistical corrections (e.g., for multiple testing), they appear unaware that enrichment results are quite sensitive to the pathway definitions that the calculation uses. Results: We show that alternative pathway definitions can alter enrichment p-values by up to nine orders of magnitude, whereas statistical corrections typically alter enrichment p-values by only two orders of magnitude. We present multiple examples where the smaller pathway definitions used in the EcoCyc database produces stronger enrichment p-values than the much larger pathway definitions used in the KEGG database; we demonstrate that to attain a given enrichment p-value, KEGG-based enrichment analyses require 1.3–2.0 times as many significantly expressed genes as does EcoCyc-based enrichment analyses. The large pathways in KEGG are problematic for another reason: they blur together multiple (as many as 21) biological processes. When such a KEGG pathway receives a high enrichment p-value, which of its component processes is perturbed is unclear, and thus the biological conclusions drawn from enrichment of large pathways are also in question. Conclusions: The choice of pathway database used in enrichment analyses can have a much stronger effect on the enrichment results than the statistical corrections used in these analyses.

Original languageEnglish (US)
Article number191
JournalBMC Genomics
Issue number1
StatePublished - Dec 2021

Bibliographical note

Funding Information:
This work was supported by award numbers GM077678 and GM080746 from the National Institute for General Medical Sciences of the National Institutes of Health. The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. The content of this article is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute for General Medical Sciences.

Publisher Copyright:
© 2021, The Author(s).


  • BioCyc
  • EcoCyc
  • Enrichment analysis
  • KEGG
  • Metabolomics
  • Over-representation analysis
  • Pathway size
  • Pathways


Dive into the research topics of 'Pathway size matters: the influence of pathway granularity on over-representation (enrichment analysis) statistics'. Together they form a unique fingerprint.

Cite this