CoChat
Sign Up
Cited by 177Bioinformatics and Genomic NetworksGene Regulatory Network AnalysisGenomics and Chromatin Dynamics

Sharing and Specificity of Co-expression Networks across 35 Human Tissues

Open access
Pierson, Emma, the GTEx Consortium, Koller, Daphne, Battle, Alexis, Mostafavi, Sara·2015·PLOS Computational Biology
DOI 10.1371/journal.pcbi.1004220PMC PMC4430528

Published version of a preprint

Open this paper in CoChat — free accountOpen PDF

Abstract

To understand the regulation of tissue-specific gene expression, the GTEx Consortium generated RNA-seq expression data for more than thirty distinct human tissues. This data provides an opportunity for deriving shared and tissue specific gene regulatory networks on the basis of co-expression between genes. However, a small number of samples are available for a majority of the tissues, and therefore statistical inference of networks in this setting is highly underpowered. To address this problem, we infer tissue-specific gene co-expression networks for 35 tissues in the GTEx dataset using a novel algorithm, GNAT, that uses a hierarchy of tissues to share data between related tissues. We show that this transfer learning approach increases the accuracy with which networks are learned. Analysis of these networks reveals that tissue-specific transcription factors are hubs that preferentially connect to genes with tissue specific functions. Additionally, we observe that genes with tissue-specific functions lie at the peripheries of our networks. We identify numerous modules enriched for Gene Ontology functions, and show that modules conserved across tissues are especially likely to have functions common to all tissues, while modules that are upregulated in a particular tissue are often instrumental to tissue-specific function. Finally, we provide a web tool, available at mostafavilab.stat.ubc.ca/GNAT, which allows exploration of gene function and regulation in a tissue-specific manner.

Verifying this paper

8 of 9 steps done · this page updates itself — you can leave and come back

  1. Registry recordVerified
  2. Full textRead the full text
  3. Reference list56 references found
  4. Reference checkChecking each cited work — 56 of 56
  5. StatisticsNeeds the full text
  6. ClaimsNeeds the full text
  7. Cited sourcesNeeds claims to check
  8. Self-checkNeeds the full text
  9. VerificationFindings verified

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 · 10.1371/journal.pcbi.1004220

Cites

56 of 56 references verified0 citation errors · 0 not checkable

Statistics

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

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • These genes tend to form a stronger connection with each other than with other genes, but remain at the periphery of the network, while the tissue-specific TFs become more central to that module.

    “These genes tend to form a stronger connection with each other than with other genes, but remain at the periphery of the network (thus having low centrality), while the tissue-specific TFs become more central to that module [11].”

    From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [11]

    The Discussion says genes with tissue-specific functions lie at the network periphery, while tissue-specific TFs tend to be hubs. The Accuracy of Networks section finds that genes sharing a Gene Ontology function are linked more often than genes that do not, but does not establish the claimed within-group connectivity specifically for tissue-specific genes. Nor does the paper establish that tissue-specific TFs become more central to a particular module, rather than to the network generally. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • Differential co-expression analysis can identify both tissue-specific signatures and shared co-expression signatures.

    “The increased availability of expression data for multiple tissues has allowed for differential co-expression analysis, which can identify both tissue-specific signatures and shared co-expression signatures [11].”

    From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [11]

  • Differential co-expression analysis has been used to identify genes underlying differences between healthy and disease samples or between different tissues, cell types or species.

    “Differential co-expression analysis has been used to identify genes underlying differences between healthy and disease samples [4, 8–10] or between different tissues [11], cell types [5] or species [132, 133].”

    From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [11]

  • In tissue-specific networks, TFs with functions specific to that tissue tend to be highly expressed together with tissue-specific genes.

    “It was then shown that in tissue-specific networks, TFs with functions specific to that tissue tend to be highly expressed together with tissue-specific genes.”

    From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [11]

Self-check

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

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

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. Schmidt, M.: Graphical Model Structure Learning with L1-Regularization. PhD thesis, University of Brit- ish Columbia (2010)
  2. Gao X., Pu D.Q., Wu Y., Xu H.: Tuning parameter selection for penalized likelihood estimation of Gaussian graphical model. Statistica Sinica 22(3), 1123 (2012)

Cited by

Sorted by citations

Related

Authors
Pierson, E., the GTEx Consortium, Koller, D., Battle, A., & Mostafavi, S.
Affiliations
  • Stanford University (US)
  • Johns Hopkins University (US)
  • University of British Columbia (CA)
Corresponding author
  • Pierson, Emma
  • Koller, Daphne
  • Battle, Alexis
  • Mostafavi, Sara
ORCID
Venue
PLOS Computational Biology
Publisher
Public Library of Science (PLoS)
Published
2015-05-13
Volume
11
Issue
5
Pages
e1004220
ISSN
  • 1553-734X (ISSN-L)
  • 1553-7358
DOI
10.1371/journal.pcbi.1004220
PMC
PMC4430528
OpenAlex
W2951920373
Cited by
177
References
56
Citations by year
2026: 5 · 2025: 8 · 2024: 11 · 2023: 15 · 2022: 19 · 2021: 26 · 2020: 23 · 2019: 19 · 2018: 9 · 2017: 16 · 2016: 20 · 2015: 6
FWCI
7.34
Type
journal article
Language
en
Open access
Licence
Locations
  • PLoS Computational Biology (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
Funders
  • University of Miami (MH101810, MH090936, MH101814, MH101822, MH090937, MH101825, MH090951, MH090948, MH101819, MH101782, DA006227, MH101820, DA033684, N01MH000028, MH090941)
  • University of Pennsylvania (MH101822)
  • Harvard University (MH101782, MH090948)
  • University of Chicago (MH101820, MH090951, MH090937, MH101825)
  • Broad Institute (HHSN268201000029C)
  • Université de Genève (MH101814, MH090941)
  • National Institutes of Health (10XS170, #HHSN261200800001E, HHSN268201000029C, 10XS171, HHSN261200800001C, 10ST1035)
  • National Heart, Lung, and Blood Institute (HHSN268201000029C, HHSN261200800001E)
  • National Institute of Mental Health (R01MH101814, R01MH101822, R01MH090937, R01MH101810, MH101782, HHSN261200800001E, R01MH101782, MH-090941, R01MH101819, R01MH101820, R01MH090948, MH090948, MH101825, R01MH101825, R01MH090941, MH101819, MH101820, MH090951, N01MH000028, R01MH090936, R01MH090951, MH101810, MH101822, MH090936, MH101814, MH090937)
  • National Institute on Drug Abuse (DA006227, R01DA033684, R01DA006227, DA033684)
  • National Human Genome Research Institute (U01HG007610, MH101825, HHSN261200800001E, R01HG004037)
  • NIH Office of the Director (HHSN261200800001E)
  • National Cancer Institute (HHSN261200800001E), HHSN268201000029C, P30CA016056)
  • National Institute of Diabetes and Digestive and Kidney Diseases (P30DK020595)
  • National Institute of Neurological Disorders and Stroke
  • Common Fund
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 › Genomics and Chromatin Dynamics
Keywords
gene co-expression network, tissue-specific gene expression, GTEx, transfer learning, network inference, tissue-specific gene function, human tissue, gene regulatory networks
MeSH terms
Algorithms, Base Sequence, Gene Expression Regulation, Humans, Models, Genetic, Organ Specificity, Gene Regulatory Networks

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