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

Published version
Peer-reviewed

Type

Article

Change log

Authors

Teschendorff, Andrew E 
Jing, Han 
Paul, Dirk S 
Virta, Joni 
Nordhausen, Klaus 

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.

Description

Keywords

Cancer, Dimensional reduction, Epigenome-wide association study, Independent component analysis, Multi-omic, Tensor, mQTL, Algorithms, DNA Copy Number Variations, DNA Methylation, Epigenesis, Genetic, Epigenomics, Gene Expression Regulation, Neoplastic, Genetic Markers, Genome-Wide Association Study, Humans, Neoplasms, Quantitative Trait Loci

Journal Title

Genome Biol

Conference Name

Journal ISSN

1474-7596
1474-760X

Volume Title

19

Publisher

Springer Science and Business Media LLC
Sponsorship
Medical Research Council (MR/L003120/1)
British Heart Foundation (None)