Tensorial blind source separation for improved analysis of multi-omic data.

Clicks: 417
ID: 56015
2018
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Abstract
There is an increased need for integrative analyses of multi-omic data. We present and benchmark a novel tensorial independent component analysis (tICA) algorithm against current state-of-the-art methods. We find that tICA outperforms competing methods in identifying biological sources of data variation at a reduced computational cost. On epigenetic data, tICA can identify methylation quantitative trait loci at high sensitivity. In the cancer context, tICA identifies gene modules whose expression variation across tumours is driven by copy-number or DNA methylation changes, but whose deregulation relative to normal tissue is independent of such alterations, a result we validate by direct analysis of individual data types.
Reference Key
teschendorff2018tensorialgenome Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Teschendorff, Andrew E;Jing, Han;Paul, Dirk S;Virta, Joni;Nordhausen, Klaus;
Journal Genome biology
Year 2018
DOI
10.1186/s13059-018-1455-8
URL
Keywords
Cancer independent component analysis dimensional reduction epigenome-wide association study multi-omic tensor mqtl

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