Cited by 1,012Bioinformatics and Genomic NetworksGene Regulatory Network AnalysisGene expression and cancer classification

Is My Network Module Preserved and Reproducible?

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Langfelder, Peter, Luo, Rui, Oldham, Michael C., Horvath, Steve·2011·PLoS Computational Biology

Abstract

In many applications, one is interested in determining which of the properties of a network module change across conditions. For example, to validate the existence of a module, it is desirable to show that it is reproducible (or preserved) in an independent test network. Here we study several types of network preservation statistics that do not require a module assignment in the test network. We distinguish network preservation statistics by the type of the underlying network. Some preservation statistics are defined for a general network (defined by an adjacency matrix) while others are only defined for a correlation network (constructed on the basis of pairwise correlations between numeric variables). Our applications show that the correlation structure facilitates the definition of particularly powerful module preservation statistics. We illustrate that evaluating module preservation is in general different from evaluating cluster preservation. We find that it is advantageous to aggregate multiple preservation statistics into summary preservation statistics. We illustrate the use of these methods in six gene co-expression network applications including 1) preservation of cholesterol biosynthesis pathway in mouse tissues, 2) comparison of human and chimpanzee brain networks, 3) preservation of selected KEGG pathways between human and chimpanzee brain networks, 4) sex differences in human cortical networks, 5) sex differences in mouse liver networks. While we find no evidence for sex specific modules in human cortical networks, we find that several human cortical modules are less preserved in chimpanzees. In particular, apoptosis genes are differentially co-expressed between humans and chimpanzees. Our simulation studies and applications show that module preservation statistics are useful for studying differences between the modular structure of networks. Data, R software and accompanying tutorials can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ModulePreservation.

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  • All ODC-specific modules were significantly preserved in an independent dataset (Z summary preservation ≥5).

    “we performed module preservation analysis20 to test the reproducibility of these modules in an independent dataset12 and found that all of the ODC-specific modules were significantly preserved (Z summary preservation ≥5).”
  • At currently available sample sizes, networks are most reproducible at module level, then hub gene level, and least at precise gene connectivity rankings or module memberships.

    “At currently available sample sizes, networks are most reproducible at a module level35,38,39, then at the hub gene level41,223 and, last, at the level of precise gene connectivity rankings or precise module memberships of genes40,49.”
  • The gene modules identified in Brain 1 were well conserved in Brain 2 as a whole, both at the level of regional gene expression patterns and by a module preservation index.

    “The gene modules identified in Brain 1 were well conserved in Brain 2 as a whole (Fig. 3b), both at the level of regional gene expression patterns (third row) and as measured by a module preservation index (fourth row) using a summary Z-statistic as described previously20.”
  • The clustering assignment from SpeakEasy overlapped significantly with WGCNA.

    “We note that the clustering assignment identified by the SpeakEasy algorithm overlapped significantly with those proposed by the frequently used WGCNA algorithm22 (Supplementary methods; Table S4)”
  • 13 of the 16 BLSA modules were preserved in the Emory network, with Zsummary scores above 2 (p<0.05).

    “Importantly, 13 of the 16 modules in the BLSA network (Fig. 4A) were preserved in the Emory network, with Zsummary scores above 2 (p<0.05) (Fig. 4A) (Langfelder et al., 2011).”

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Authors
Langfelder, P., Luo, R., Oldham, M. C., & Horvath, S.
Affiliations
University of California, Los Angeles (US)
Corresponding author
Horvath, Steve
Venue
PLoS Computational Biology
Published
2011-01-20
Volume
7
Issue
1
Pages
e1001057
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
OpenAlex
W1974033269
Cited by
1,012
References
60
Citations by year
2026: 83 · 2025: 74 · 2024: 77 · 2023: 97 · 2022: 78 · 2021: 119 · 2020: 99 · 2019: 77 · 2018: 73 · 2017: 61 · 2016: 56 · 2015: 30 · 2014: 28 · 2013: 26 · 2012: 24
FWCI
11.23
Type
journal article
Language
en
Open access
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • eScholarship (California Digital Library) (submittedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • Figshare (submittedVersion, cc-by)
Funders
  • National Cancer Institute (P50CA092131, P30CA016042)
  • National Institute of Allergy and Infectious Diseases (U19AI063603)
  • National Institute of Diabetes and Digestive and Kidney Diseases (R01DK072206, DK-072206)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords
reproducibility, gene co-expression network, correlation networks, cholesterol biosynthesis, apoptosis-related genes
MeSH terms
Animals, Brain, Female, Humans, Male, Reproducibility of Results, Gene Expression, Mice

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