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Gene networks in cancer are biased by aneuploidies and sample impurities.
Biochim. Biophys. Acta-Gene Regul. Mech. 1863:194444 (2020)
Gene regulatory network inference is a standard technique for obtaining structured regulatory information from, for instance, gene expression measurements. Methods performing this task have been extensively evaluated on synthetic, and to a lesser extent real data sets. In contrast to these test evaluations, applications to gene expression data of human cancers are often limited by fewer samples and more potential regulatory links, and are biased by copy number aberrations as well as cell mixtures and sample impurities. Here, we take networks inferred from TCGA cohorts as an example to show that (1) transcription factor annotations are essential to obtain reliable networks, and (2) even for state of the art methods, we expect that between 20 and 80% of edges are caused by copy number changes and cell mixtures rather than transcription factor regulation.
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Publication type
Article: Journal article
Document type
Review
Keywords
Gene Regulatory Networks ; Cancer ; Method Comparison ; Aneuploidy; Inference; Widespread
ISSN (print) / ISBN
1874-9399
e-ISSN
1876-4320
Quellenangaben
Volume: 1863,
Issue: 6,
Article Number: 194444
Publisher
Elsevier
Publishing Place
Radarweg 29, 1043 Nx Amsterdam, Netherlands
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute of Computational Biology (ICB)