Dataset Information

Hierarchical HotNet: identifying hierarchies of altered subnetworks.

ABSTRACT:

Motivation

The analysis of high-dimensional 'omics data is often informed by the use of biological interaction networks. For example, protein-protein interaction networks have been used to analyze gene expression data, to prioritize germline variants, and to identify somatic driver mutations in cancer. In these and other applications, the underlying computational problem is to identify altered subnetworks containing genes that are both highly altered in an 'omics dataset and are topologically close (e.g. connected) on an interaction network.

Results

We introduce Hierarchical HotNet, an algorithm that finds a hierarchy of altered subnetworks. Hierarchical HotNet assesses the statistical significance of the resulting subnetworks over a range of biological scales and explicitly controls for ascertainment bias in the network. We evaluate the performance of Hierarchical HotNet and several other algorithms that identify altered subnetworks on the problem of predicting cancer genes and significantly mutated subnetworks. On somatic mutation data from The Cancer Genome Atlas, Hierarchical HotNet outperforms other methods and identifies significantly mutated subnetworks containing both well-known cancer genes and candidate cancer genes that are rarely mutated in the cohort. Hierarchical HotNet is a robust algorithm for identifying altered subnetworks across different 'omics datasets.

Availability and implementation

http://github.com/raphael-group/hierarchical-hotnet.

Supplementary information

Supplementary material are available at Bioinformatics online.

SUBMITTER: Reyna MA

PROVIDER: S-EPMC6129270 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Publications

Hierarchical HotNet: identifying hierarchies of altered subnetworks.

Reyna Matthew A MA Leiserson Mark D M MDM Raphael Benjamin J BJ

Bioinformatics (Oxford, England) 20180901 17

<h4>Motivation</h4>The analysis of high-dimensional 'omics data is often informed by the use of biological interaction networks. For example, protein-protein interaction networks have been used to analyze gene expression data, to prioritize germline variants, and to identify somatic driver mutations in cancer. In these and other applications, the underlying computational problem is to identify altered subnetworks containing genes that are both highly altered in an 'omics dataset and are topologi ...[more]

PMID: 30423088

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Dataset Information

Hierarchical HotNet: identifying hierarchies of altered subnetworks.

Motivation

Results

Availability and implementation

Supplementary information

Publications

Hierarchical HotNet: identifying hierarchies of altered subnetworks.

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