Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 718

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Enrichment or depletion of a GO category within a class of genes: which test?

Open access
Rivals, Isabelle, Personnaz, Léon, Taing, Lieng, Potier, Marie-Claude·2007·Bioinformatics

Abstract

Motivation: A number of available program packages determine the significant enrichments and/or depletions of GO categories among a class of genes of interest. Whereas a correct formulation of the problem leads to a single exact null distribution, these GO tools use a large variety of statistical tests whose denominations often do not clarify the underlying P-value computations. Summary: We review the different formulations of the problem and the tests they lead to: the binomial, χ2, equality of two probabilities, Fisher's exact and hypergeometric tests. We clarify the relationships existing between these tests, in particular the equivalence between the hypergeometric test and Fisher's exact test. We recall that the other tests are valid only for large samples, the test of equality of two probabilities and the χ2-test being equivalent. We discuss the appropriateness of one- and two-sided P-values, as well as some discreteness and conservatism issues. Contact: isabelle.rivals@espci.fr Supplementary information: Supplementary data are available at Bioinformatics online.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1093/bioinformatics/btl633

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 3 references

Research works printed but not linked: 30

  1. Agresti, 1992, A survey of exact inference for contingency tables, Stat. Sci., 7, 131
  2. Agresti, 2001, On small-sample confidence intervals for parameters in discrete distributions, Biometrics, 57, 963, 10.1111/j.0006-341X.2001.00963.x · DOI
  3. Agresti, 2006, Reducing conservatism of exact small-sample methods of inference for discrete data, 10.1007/978-3-7908-1709-6_19 · DOI
  4. Al-Sharour, 2004, FatiGO: A web tool for finding significant associations of Gene Ontology terms with groups of genes, Bioinformatics, 20, 578, 10.1093/bioinformatics/btg455 · DOI · Paper page
  5. Beißbarth, 2004, GOstat: find statistically overrepresented Gene Ontologies within & group of genes, Bioinformatics, 20, 1464, 10.1093/bioinformatics/bth088 · DOI · Paper page
  6. Castillo-Davis, 2003, GeneMerge–post-genomics analysis, data mining, and hypothesis testing, Bioinformatics, 19, 891, 10.1093/bioinformatics/btg114 · DOI · Paper page
  7. Cheng, 2004, NetAffx Gene Ontology Mining Tool: a visual approach for microarray data analysis, Bioinformatics, 20, 1462, 10.1093/bioinformatics/bth087 · DOI
  8. Draghici, 2003, Global functional profiling of gene expression, Genomics, 81, 98
  9. Dunne, 1996, Two-sided P-values from discrete asymmetric distributions based on uniformly most powerful unbiased tests, The Statistician, 45, 397, 10.2307/2988542 · DOI
  10. Ernst, 2005, Clustering short time series gene expression data, Bioinformatics, 21, i159, 10.1093/bioinformatics/bti1022 · DOI · Paper page
  11. Fisher, 1935, The logic of inductive inference, J. Royal Stat. Soc., 98, 39, 10.2307/2342435 · DOI · Paper page
  12. Gibbons, 1975, P-values: interpretation and methodology, Am. Stat., 29, 20
  13. Hosack, 2003, Identifying biological themes within lists of genes with EASE, Genome Biol., 4, R70, 10.1186/gb-2003-4-10-r70 · DOI · Paper page
  14. Khatri, 2002, Profiling gene expression utilizing onto-express, Genomics, 79, 266, 10.1006/geno.2002.6698 · DOI · Paper page
  15. Khatri, 2005, Ontological analysis of gene expression data: current tools, limitations, and open problems, Bioinformatics, 21, 3587, 10.1093/bioinformatics/bti565 · DOI · Paper page
  16. Lehman, 1986, Testing Statistical Hypotheses, 10.1007/978-1-4757-1923-9 · DOI · Paper page
  17. Maere, 2005, BiNGO: a Cytoscape plugin to assass overrepresentation of Gene Ontology categories in Biological Networks, Bioinformatics, 21, 3448, 10.1093/bioinformatics/bti551 · DOI · Paper page
  18. Martin, 2004, GOToolbox: functional analysis of gene datasets based on Gene Ontology, Genome Biol., 5, R101, 10.1186/gb-2004-5-12-r101 · DOI · Paper page
  19. Masseroli, 2004, GFINDer: Genome Function INtegrated Discoverer through dynamic annotation, statistical analysisn and mining, Nucleic Acids Res., 32, W293, 10.1093/nar/gkh432 · DOI
  20. Mehta, 1998, Exact inference for categorical data, Encyclopedia of Biostatistics, Vol. 2, 1411
  21. Mood, 1974, Introduction to the Theory of Statistics
  22. Newman, 2005, L2L: a simple tool for discovering the hidden significance in microarray expression data, Genome Biol., 6, R8, 10.1186/gb-2005-6-9-r81 · DOI
  23. Pasquier, 2004, THEA: ontology-driven analysis of microarray, Bioinformatics, 20, 2636, 10.1093/bioinformatics/bth295 · DOI
  24. Robinson, 2002, FunSpec: a web-based cluster interpreter for yeast, BMC Bioinformatics, 3, 35, 10.1186/1471-2105-3-35 · DOI · Paper page
  25. Shah, 2004, CLENCH: a program for calculating Cluster ENriCHment using the Gene Ontology, Bioinformatics, 20, 1196, 10.1093/bioinformatics/bth056 · DOI
  26. Yates, 1984, Test of significance for 2x2 contingency tables, J. Royal Stat. Soc. Series A, 147, 426, 10.2307/2981577 · DOI · Paper page
  27. Young, 2005, Ontology Traverser: an R package for GO analysis, Bioinformatics, 21, 275, 10.1093/bioinformatics/bth495 · DOI
  28. Zeeberg, 2003, GoMiner: a resource for biological interpretation of genomic and proteomic data, Genome Biol., 4, R28, 10.1186/gb-2003-4-4-r28 · DOI · Paper page
  29. Zhang, 2004, GOTree Machine (GOTM): a web-based platform for interpreting sets of iinteresting genes using Gene Ontology hierarchies, BMC Bioinformatics, 5, 16, 10.1186/1471-2105-5-16 · DOI · Paper page
  30. Zhong, 2004, GoSurfer: a graphical interactive tool for comparative analysis of large gene sets in gene ontology space, Appl. Bioinformatics, 3, 261, 10.2165/00822942-200403040-00009 · DOI

Cited by

See all 718

Metadata

Authors
Rivals, I., Personnaz, L., Taing, L., & Potier, M.-C.
Venue
Bioinformatics
Published
2006-12-20
Volume
23
Issue
4
Pages
401-407
ISSN
  • 1367-4803 (ISSN-L)
  • 1367-4811
OpenAlex
W2152274187
Cited by
718
References
3
FWCI
7.61
Type
journal article
Language
en
Open access
hybrid

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Enrichment or depletion of a GO category within a class of genes: which test? https://app.cochat.ai/papers/10.1093/bioinformatics/btl633
The paper
Rivals, I., Personnaz, L., Taing, L., & Potier, M.-C. (2007). Enrichment or depletion of a GO category within a class of genes: which test? Bioinformatics. https://doi.org/10.1093/bioinformatics/btl633

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.