Sharing and Specificity of Co-expression Networks across 35 Human Tissues.
Sharing and Specificity of Co-expression Networks across 35 Human Tissues.
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DOI：
10.1371/journal.pcbi.1004220
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发表时间：
2015-05
影响因子：
4.3
通讯作者：
Dermitzakis ET
中科院分区：
文献类型：
--
作者：
Pierson E;GTEx Consortium;Koller D;Battle A;Mostafavi S;Ardlie KG;Getz G;Wright FA;Kellis M;Volpi S;Dermitzakis ET
为了了解组织特异性基因表达的调控，GTEx Consortium生成了30多种不同人类组织的RNA-seq表达数据。这些数据为基于基因间的共表达推导出共享的和组织特异性的基因调控网络提供了机会。然而，对于大多数组织来说，只有少量的样本可用，因此在这种情况下，网络的统计推断非常不足。为了解决这个问题，我们使用一种新的算法GNAT来推断GTEx数据集中35个组织的组织特异性基因共表达网络，该算法使用组织层次结构来在相关组织之间共享数据。我们表明，这种迁移学习方法提高了网络学习的准确性。对这些网络的分析表明，组织特异性转录因子是优先连接到具有组织特异性功能的基因的枢纽。此外，我们观察到具有组织特异性功能的基因位于我们网络的外围。我们确定了许多模块丰富的基因本体功能，并表明，在组织中保守的模块特别可能具有共同的所有组织的功能，而在特定组织中上调的模块往往是组织特异性功能的工具。最后，我们提供了一个网络工具，可在mostafavilab.stat.ubc.ca/GNAT，它允许探索基因的功能和调控的组织特异性的方式。不同组织中的细胞在相同的DNA下执行非常不同的功能。这需要组织特异性基因表达和调节;了解这种组织特异性通常有助于了解复杂疾病。在这里，我们使用组织特异性基因表达数据来学习35个人体组织的组织特异性基因调控网络，其中如果两个基因的表达水平相关，则它们是相互关联的。准确地学习这样的网络是困难的，因为基因之间可能存在大量的联系，而样本数量很少。我们提出了一种新的算法，通过在相似组织之间共享数据来解决这个问题，并表明这提高了网络学习的准确性。我们提供了一个网络工具来探索这些网络，使用户能够以基因或组织为中心的方式提出不同的查询，并促进对基因功能和调控的探索。
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.
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