Refereed journal article or data article (A1)
Tensorial blind source separation for improved analysis of multi-omic data
List of Authors: Teschendorff AE, Jing H, Paul DS, Virta J, Nordhausen K
Publisher: BIOMED CENTRAL LTD
Publication year: 2018
Journal: Genome Biology
Journal name in source: GENOME BIOLOGY
Journal acronym: GENOME BIOL
Volume number: 19
Number of pages: 18
ISSN: 1474-760X
DOI: http://dx.doi.org/10.1186/s13059-018-1455-8
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/32116144
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.
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.
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