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

Pierson E, GTEx ConsortiumKoller D, Battle A, Mostafavi S, Ardlie KG, Getz G, Wright FA, Kellis M, Volpi S, Dermitzakis ET - PLoS Comput. Biol. (2015)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Stanford University, Stanford, California, United States of America.

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.

No MeSH data available.


Important principles of tissue-specificity.a) Tissue-specific transcription factors (circled in blue) have higher expression levels (green) in tissues they are specific to, and those that change most dramatically in expression are most likely to be essential genes. b) Tissue-specific transcription factors connect to and upregulate genes with tissue-specific function (circled in red), which in turn connect to each other. c) Transcription factors lie at the centers of networks; genes with tissue-specific function and enriched modules lie at the network peripheries. d) Modules shared across tissues are more likely to be enriched for Gene Ontology functions, and tend to have functions common to all tissues like cell division.
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pcbi.1004220.g004: Important principles of tissue-specificity.a) Tissue-specific transcription factors (circled in blue) have higher expression levels (green) in tissues they are specific to, and those that change most dramatically in expression are most likely to be essential genes. b) Tissue-specific transcription factors connect to and upregulate genes with tissue-specific function (circled in red), which in turn connect to each other. c) Transcription factors lie at the centers of networks; genes with tissue-specific function and enriched modules lie at the network peripheries. d) Modules shared across tissues are more likely to be enriched for Gene Ontology functions, and tend to have functions common to all tissues like cell division.

Mentions: Tissue-specific transcription factors (tsTFs) are important in defining tissue-specific phenotypes and mutations affecting tsTFs are enriched in loci associated with disease [2, 27]. We used our networks to analyze the role tsTFs play in tissue specificity using a collection of 203 known tsTFs (S2 Table) and 88 general TFs (gTFs) defined in [8]. We provide a schematic illustration of important conclusions of our analysis in Fig 4 and a tabular summary in Table 1.


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

Pierson E, GTEx ConsortiumKoller D, Battle A, Mostafavi S, Ardlie KG, Getz G, Wright FA, Kellis M, Volpi S, Dermitzakis ET - PLoS Comput. Biol. (2015)

Important principles of tissue-specificity.a) Tissue-specific transcription factors (circled in blue) have higher expression levels (green) in tissues they are specific to, and those that change most dramatically in expression are most likely to be essential genes. b) Tissue-specific transcription factors connect to and upregulate genes with tissue-specific function (circled in red), which in turn connect to each other. c) Transcription factors lie at the centers of networks; genes with tissue-specific function and enriched modules lie at the network peripheries. d) Modules shared across tissues are more likely to be enriched for Gene Ontology functions, and tend to have functions common to all tissues like cell division.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4430528&req=5

pcbi.1004220.g004: Important principles of tissue-specificity.a) Tissue-specific transcription factors (circled in blue) have higher expression levels (green) in tissues they are specific to, and those that change most dramatically in expression are most likely to be essential genes. b) Tissue-specific transcription factors connect to and upregulate genes with tissue-specific function (circled in red), which in turn connect to each other. c) Transcription factors lie at the centers of networks; genes with tissue-specific function and enriched modules lie at the network peripheries. d) Modules shared across tissues are more likely to be enriched for Gene Ontology functions, and tend to have functions common to all tissues like cell division.
Mentions: Tissue-specific transcription factors (tsTFs) are important in defining tissue-specific phenotypes and mutations affecting tsTFs are enriched in loci associated with disease [2, 27]. We used our networks to analyze the role tsTFs play in tissue specificity using a collection of 203 known tsTFs (S2 Table) and 88 general TFs (gTFs) defined in [8]. We provide a schematic illustration of important conclusions of our analysis in Fig 4 and a tabular summary in Table 1.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Stanford University, Stanford, California, United States of America.

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.

No MeSH data available.