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Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks

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Wolfe, Cecily J, Kohane, Isaac S, Butte, Atul J·2005·BMC Bioinformatics

DOI 10.1186/1471-2105-6-227PMC PMC1239911

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Abstract

Background: Biological processes are carried out by coordinated modules of interacting molecules. As clustering methods demonstrate that genes with similar expression display increased likelihood of being associated with a common functional module, networks of coexpressed genes provide one framework for assigning gene function. This has informed the guilt-by-association (GBA) heuristic, widely invoked in functional genomics. Yet although the idea of GBA is accepted, the breadth of GBA applicability is uncertain. Results: We developed methods to systematically explore the breadth of GBA across a large and varied corpus of expression data to answer the following question: To what extent is the GBA heuristic broadly applicable to the transcriptome and conversely how broadly is GBA captured by a priori knowledge represented in the Gene Ontology (GO)? Our study provides an investigation of the functional organization of five coexpression networks using data from three mammalian organisms. Our method calculates a probabilistic score between each gene and each Gene Ontology category that reflects coexpression enrichment of a GO module. For each GO category we use Receiver Operating Curves to assess whether these probabilistic scores reflect GBA. This methodology applied to five different coexpression networks demonstrates that the signature of guilt-by-association is ubiquitous and reproducible and that the GBA heuristic is broadly applicable across the population of nine hundred Gene Ontology categories. We also demonstrate the existence of highly reproducible patterns of coexpression between some pairs of GO categories. Conclusion: We conclude that GBA has universal value and that transcriptional control may be more modular than previously realized. Our analyses also suggest that methodologies combining coexpression measurements across multiple genes in a biologically-defined module can aid in characterizing gene function or in characterizing whether pairs of functions operate together.

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Figure 1 Schematic representation of the steps in our analyses. (a) Example flow chart of the different steps for calculating gene set coexpression enrichment Pe values between each of the 6624 genes in the multi-species network and 902 GO sets. For each gene mi we use the hypergeometric distribution to calculate a coexpression enrichment Pe-value (Pe(mi, gj)) for whether GO set gj was significantly overrepresented in the top 250 genes with smallest Pc-values to mi. (b) The four steps in our analyses. 1. A coexpression network is generated with Pc values (multi-species network) or correlation coefficients (single-species network) scoring coexpression between gene pairs. 2. Coexpression enrichment Pe values are calculated between each gene and each GO category, such as between GO category 1 and genes A, B, and C and between GO category 2 and genes A, B, and C. 3. A score reflecting GBA is calculated for each GO category (e.g., GO category 1). 4. The interrelationship between pairs of GO categories is quantified, such as that between GO category 1 and GO category 2, which are sibling categories in a Gene Ontology graph, sharing GO category 3 as a common parent.CC BY · PubMed Central

Figure 2 Examples from the multi-species network. (a-d) Self-diagnostic Receiver Operating Characteristic (ROC) curves for the GO categories shown above.CC BY · PubMed Central

Figure 3 Histograms of self-diagnostic ROC areas for the multi-species network. (a) Histogram for biological process GO categories. (b) Histogram for cellular component GO categories. (c) Histogram for molecular function GO categories. (d) Histogram for randomized GO sets. (d) Histogram for a randomized multi-species coexpression network.CC BY · PubMed Central

Figure 4 Histograms of cross-diagnostic ROC areas for the multi-species network. (a) Histogram of ROC areas for whether descendent Pe-values are diagnostic of parent sets. (b) Histogram of ROC areas for cross pairing of sibling categories. (c) Histogram of ROC areas for all cross pairings of categories (excluding parent-descendent pairs) with distances of 3–16 in a GO graph. GO organizes categories as nodes on a graph and calculates the distance between category pairs on the same graph as the minimum number of arcs needed to traverse from one category node to another on the graph. For example, a parent and its child are separated by a distance of one and siblings are separated by distances of two. (d) Histogram of ROC areas for all cross pairings of categories (excluding parent-descendent pairs) with distances of 3–16 in a GO graph, created using randomized GO sets. (e) Histogram of cross-diagnostic ROC areas between GO category pairs (excluding parent-descendent pairs) in the subgraph below immune response. (f) Histogram of cross-diagnostic ROC areas between GO category pairs (excluding parent-descendent pairs) in the subgraph below cell cycle.CC BY · PubMed Central

Figure 5 Plots of self-diagnostic ROC areas from the multi-species network (x-axis) versus ROC areas from a single-species network (y-axis) for each GO category. Each panel examines one of the single-species networks, created using microarrays from the following Affymetrix platforms: HG-U133A (human), HG-U95A (human), MG-U74A (mouse), and RG-U34A (rat). Correlation coefficients are noted in the upper left corner of the plots.CC BY · PubMed Central

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Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes
Mootha, V. K., Lindgren, C. M., Eriksson, K.-F., Subramanian, A., Sihag, S., Lehar, J., Puigserver, P., Carlsson, E., Ridderstråle, M., Laurila, E., et al. (2003) · Nature Genetics · doi:10.1038/ng1180 · Cited by 10,197
From molecular to modular cell biology.
Hartwell, L. H., Hopfield, J. J., Leibler, S., & Murray, A. W. (1999) · Nature · doi:10.1038/35011540 · Cited by 3,632
Functional Discovery via a Compendium of Expression Profiles
Hughes, T. R., Marton, M. J., Jones, A. R., Roberts, C. J., Stoughton, R. B., Armour, C. D., Bennett, H. A., Coffey, E. M., Dai, H. A., He, Y. D., et al. (2000) · Cell · doi:10.1016/s0092-8674(00)00015-5 · Cited by 2,534
Systematic determination of genetic network architecture
Tavazoie, S., Hughes, J. D., Campbell, M. J., Cho, R. J., & Church, G. M. (1999) · Nature Genetics · doi:10.1038/10343 · Cited by 2,509
Exploring the new world of the genome with DNA microarrays
Brown, P. O., & Botstein, D. (1999) · Nature Genetics · doi:10.1038/4462 · Cited by 2,430
A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules
Stuart, J. M., Segal, E., Koller, D., & Kim, S. K. (2003) · Science · doi:10.1126/science.1087447 · Cited by 2,274
Computational analysis of microarray data
Quackenbush, J. (2001) · Nature Reviews Genetics · doi:10.1038/35076576 · Cited by 1,481

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Authors: Wolfe, C. J., Kohane, I. S., & Butte, A. J.
Affiliations: Harvard University (US)
Corresponding author: Wolfe, Cecily J
ORCID: Wolfe, Cecily J
Kohane, Isaac S
Butte, Atul J
Venue: BMC Bioinformatics
Publisher: Springer Science and Business Media LLC
Published: 2005-09-14
Volume: 6
Issue: 1
Pages: 227-227
ISSN: 1471-2105 (ISSN-L)
DOI: 10.1186/1471-2105-6-227
PMC: PMC1239911
OpenAlex: W2159767718
Cited by: 422
References: 24
Citations by year: 2026: 5 · 2025: 10 · 2024: 12 · 2023: 14 · 2022: 28 · 2021: 39 · 2020: 40 · 2019: 29 · 2018: 24 · 2017: 19 · 2016: 17 · 2015: 20 · 2014: 18 · 2013: 20 · 2012: 20
FWCI: 5.90
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: BMC Bioinformatics (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders: Pediatric Endocrine Society
National Institutes of Health (LM008748)
National Institute of Diabetes and Digestive and Kidney Diseases (R01 DK62948, K12DK63696, K12DK063696, R01DK062948)
U.S. National Library of Medicine (U54LM008748, 5T15 LM07092, 2 TA5 LM 07092-11, T15LM007092)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords: guilt by association, gene co-expression network, Gene Ontology, gene function prediction, functional modules, probabilistic scoring, transcriptome, transcriptional regulation
MeSH terms: Animals, Bone Development, Feeding Behavior, Humans, Phylogeny, Probability, ROC Curve, Species Specificity, Reproducibility of Results, Receptors, Neuropeptide, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Synteny, Mice, Rats

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Systematic survey reveals general applicability of "guilt-by-association" within gene coexpression networks

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