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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

Network accuracy as measured by 5-fold cross validation.Learning networks independently corresponds to setting λp = 0 (the bottom left corner of each graph); the y-axis is the improvement in log likelihood over baseline. Our method improved on this baseline for all three gene sets we experimented with. The baseline of learning a single network for all tissues cannot be shown on this graph because its log likelihood is so low; we dropped it from further consideration in our analysis. The differing scales on the y-axes are due to the different sizes of the gene sets.
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pcbi.1004220.g003: Network accuracy as measured by 5-fold cross validation.Learning networks independently corresponds to setting λp = 0 (the bottom left corner of each graph); the y-axis is the improvement in log likelihood over baseline. Our method improved on this baseline for all three gene sets we experimented with. The baseline of learning a single network for all tissues cannot be shown on this graph because its log likelihood is so low; we dropped it from further consideration in our analysis. The differing scales on the y-axes are due to the different sizes of the gene sets.

Mentions: We used 5-fold cross-validation to evaluate our algorithm: for each tissue, we randomly divided our samples into five groups, learned networks based on samples from four of the five groups, and measured the accuracy of each network (quantified by the log likelihood on the held out test data) using the remaining group. We compared the performance of our method to two baselines: learning a network for each tissue independently, or learning a single network for all tissues. We observed a higher log likelihood on the held out test set using our approach as compared to the two baselines on three different gene sets of increasing sizes (Fig 3), indicating that the transfer learning approach resulted in a more robust estimation of the networks.


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)

Network accuracy as measured by 5-fold cross validation.Learning networks independently corresponds to setting λp = 0 (the bottom left corner of each graph); the y-axis is the improvement in log likelihood over baseline. Our method improved on this baseline for all three gene sets we experimented with. The baseline of learning a single network for all tissues cannot be shown on this graph because its log likelihood is so low; we dropped it from further consideration in our analysis. The differing scales on the y-axes are due to the different sizes of the gene sets.
© Copyright Policy
Related In: Results  -  Collection

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

pcbi.1004220.g003: Network accuracy as measured by 5-fold cross validation.Learning networks independently corresponds to setting λp = 0 (the bottom left corner of each graph); the y-axis is the improvement in log likelihood over baseline. Our method improved on this baseline for all three gene sets we experimented with. The baseline of learning a single network for all tissues cannot be shown on this graph because its log likelihood is so low; we dropped it from further consideration in our analysis. The differing scales on the y-axes are due to the different sizes of the gene sets.
Mentions: We used 5-fold cross-validation to evaluate our algorithm: for each tissue, we randomly divided our samples into five groups, learned networks based on samples from four of the five groups, and measured the accuracy of each network (quantified by the log likelihood on the held out test data) using the remaining group. We compared the performance of our method to two baselines: learning a network for each tissue independently, or learning a single network for all tissues. We observed a higher log likelihood on the held out test set using our approach as compared to the two baselines on three different gene sets of increasing sizes (Fig 3), indicating that the transfer learning approach resulted in a more robust estimation of the networks.

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