Teschendorff, AE;
Jing, H;
Paul, DS;
Virta, J;
Nordhausen, K;
(2018)
Tensorial blind source separation for improved analysis of multi-omic data.
Genome Biology
, 19
(1)
, Article 76. 10.1186/s13059-018-1455-8.
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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.
| Type: | Article |
|---|---|
| Title: | Tensorial blind source separation for improved analysis of multi-omic data |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1186/s13059-018-1455-8 |
| Publisher version: | https://doi.org/10.1186/s13059-018-1455-8 |
| Language: | English |
| Additional information: | © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Multi-omic, Tensor, Dimensional reduction, Independent component analysis, mQTL, Epigenome-wide association study, Cancer |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10050867 |
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