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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.


Related in: MedlinePlus

The hierarchy of tissues which is used as the basis for learning networks for each tissue.The hierarchy was created using hierarchical clustering: for each tissue, the mean expression of each gene in the tissue was computed, and tissues with similar gene expression patterns were merged into clusters. Lower branching points represent clusters with more similar gene expression patterns. Many biologically plausible clusters are apparent: the brain and non-brain cluster, and clusters for the basal ganglia, cortex, adipose tissue, heart, artery, and skin.
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pcbi.1004220.g001: The hierarchy of tissues which is used as the basis for learning networks for each tissue.The hierarchy was created using hierarchical clustering: for each tissue, the mean expression of each gene in the tissue was computed, and tissues with similar gene expression patterns were merged into clusters. Lower branching points represent clusters with more similar gene expression patterns. Many biologically plausible clusters are apparent: the brain and non-brain cluster, and clusters for the basal ganglia, cortex, adipose tissue, heart, artery, and skin.

Mentions: 1. Learning a hierarchy. A tissue hierarchy was constructed using agglomerative hierarchical clustering on the mean gene expression levels for the 35 tissues (Fig 1). Since the rest of the algorithm was independent of the construction of the hierarchy, the method would also work with a hierarchy based on prior knowledge or on some other measure of dataset similarity.


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)

The hierarchy of tissues which is used as the basis for learning networks for each tissue.The hierarchy was created using hierarchical clustering: for each tissue, the mean expression of each gene in the tissue was computed, and tissues with similar gene expression patterns were merged into clusters. Lower branching points represent clusters with more similar gene expression patterns. Many biologically plausible clusters are apparent: the brain and non-brain cluster, and clusters for the basal ganglia, cortex, adipose tissue, heart, artery, and skin.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004220.g001: The hierarchy of tissues which is used as the basis for learning networks for each tissue.The hierarchy was created using hierarchical clustering: for each tissue, the mean expression of each gene in the tissue was computed, and tissues with similar gene expression patterns were merged into clusters. Lower branching points represent clusters with more similar gene expression patterns. Many biologically plausible clusters are apparent: the brain and non-brain cluster, and clusters for the basal ganglia, cortex, adipose tissue, heart, artery, and skin.
Mentions: 1. Learning a hierarchy. A tissue hierarchy was constructed using agglomerative hierarchical clustering on the mean gene expression levels for the 35 tissues (Fig 1). Since the rest of the algorithm was independent of the construction of the hierarchy, the method would also work with a hierarchy based on prior knowledge or on some other measure of dataset similarity.

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.


Related in: MedlinePlus