Sharing and Specificity of Co-expression Networks across 35 Human Tissues
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...
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| Published in: | PLoS computational biology Vol. 11; no. 5; p. e1004220 |
|---|---|
| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
United States
Public Library of Science
01.05.2015
Public Library of Science (PLoS) |
| Subjects: | |
| ISSN: | 1553-7358, 1553-734X, 1553-7358 |
| Online Access: | Get full text |
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| 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. |
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| AbstractList | 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. 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. Cells in different tissues perform very different functions with the same DNA. This requires tissue-specific gene expression and regulation; understanding this tissue-specificity is often instrumental to understanding complex diseases. Here, we use tissue-specific gene expression data to learn tissue-specific gene regulatory networks for 35 human tissues, where two genes are linked if their expression levels are correlated. Learning such networks accurately is difficult because of the large number of possible links between genes and small number of samples. We propose a novel algorithm that combats this problem by sharing data between similar tissues and show that this increases the accuracy with which networks are learned. We provide a web tool for exploring these networks, enabling users to pose diverse queries in a gene- or tissue-centric manner, and facilitating explorations into gene function and regulation. 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.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. 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. |
| Audience | Academic |
| Author | Pierson, Emma Mostafavi, Sara Battle, Alexis Koller, Daphne |
| AuthorAffiliation | Department of Computer Science, Stanford University, Stanford, California, United States of America Thomas Jefferson University, UNITED STATES |
| AuthorAffiliation_xml | – name: Thomas Jefferson University, UNITED STATES – name: Department of Computer Science, Stanford University, Stanford, California, United States of America |
| Author_xml | – sequence: 1 givenname: Emma surname: Pierson fullname: Pierson, Emma – sequence: 2 givenname: Daphne surname: Koller fullname: Koller, Daphne – sequence: 3 givenname: Alexis surname: Battle fullname: Battle, Alexis – sequence: 4 givenname: Sara surname: Mostafavi fullname: Mostafavi, Sara |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/25970446$$D View this record in MEDLINE/PubMed |
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| ContentType | Journal Article |
| Contributor | Deluca, David S Sammeth, Michael Goldmann, Jakob Syron, John Sullivan, Susan Valentino, Kimberly M McCarthy, Mark I Meng, Yan Monlong, Jean Undale, Anita H Foster, Barbara A Lek, Monkol Lin, Luan Shablin, Andrey A Guigo, Roderic Bridge, Jason P Wen, Xiaoquan Hirschhorn, Joel Zhu, Jun Battle, Alexis Ward, Lucas D Winckler, Wendy Koller, Daphne Huang, Tao Wright, Fred A Lappalainen, Tuuli Tabor, David Zhou, Yi-Hui Wu, Shenpei Long, Quan Stephens, Matthew Cox, Nancy J Kheradpour, Pouya Sobin, Leslie H Buia, Stephen A Sullivan, Timothy J Brown, Amanda Thomas, Jeffrey A Hariharan, Pushpa Ongen, Halit Walters, Gary D Qi, Liqun Gamazon, Eric R Fleming, Johnelle Gillard, Bryan M Rivas, Manuel A Rohrer, Daniel C Tu, Zhidong Mosavel, Magboeba Iriarte, Benjamin Segrè, Ayellet V Moser, Michael T Ramsey, Kimberly Rusyn, Ivan Shad, Saboor Ardlie, Kristin G Traino, Heather Liu, Jun Shive, Charles Mestichelli, Bernadette Flutre, Timothée Nicolae, Dan L Salvatore, Mike Ferreira, Pedro G Palmer, Cameron D MacArthur, Daniel G Branton, Philip Pritchard, Jonathan K Zhang, |
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| Copyright | COPYRIGHT 2015 Public Library of Science 2015 Pierson et al 2015 Pierson et al 2015 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Pierson E, the GTEx Consortium, Koller D, Battle A, Mostafavi S (2015) Sharing and Specificity of Co-expression Networks across 35 Human Tissues. PLoS Comput Biol 11(5): e1004220. doi:10.1371/journal.pcbi.1004220 |
| Copyright_xml | – notice: COPYRIGHT 2015 Public Library of Science – notice: 2015 Pierson et al 2015 Pierson et al – notice: 2015 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Pierson E, the GTEx Consortium, Koller D, Battle A, Mostafavi S (2015) Sharing and Specificity of Co-expression Networks across 35 Human Tissues. PLoS Comput Biol 11(5): e1004220. doi:10.1371/journal.pcbi.1004220 |
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| DOI | 10.1371/journal.pcbi.1004220 |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Current address: Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, United States of America Current address: Department of Statistics and Department of Medical Genetics, University of British Columbia, Vancouver, Canada Conceived and designed the experiments: DK AB SM. Performed the experiments: EP. Analyzed the data: EP. Wrote the paper: EP AB SM. The authors have declared that no competing interests exist. |
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| SubjectTerms | Algorithms Annotations Base Sequence Colleges & universities Gene expression Gene Expression Regulation Gene Regulatory Networks Genomics Humans Identification and classification Keywords Methods Models, Genetic Organ Specificity - genetics RNA sequencing Transcription factors |
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| Title | Sharing and Specificity of Co-expression Networks across 35 Human Tissues |
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