Tensorial blind source separation for improved analysis of multi-omic data
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 d...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2018-06-01
|
Series: | Genome Biology |
Subjects: | |
Online Access: | http://link.springer.com/article/10.1186/s13059-018-1455-8 |
id |
doaj-09fa155d91294536b1cb6f1dfd037a7e |
---|---|
record_format |
Article |
spelling |
doaj-09fa155d91294536b1cb6f1dfd037a7e2020-11-25T00:21:02ZengBMCGenome Biology1474-760X2018-06-0119111810.1186/s13059-018-1455-8Tensorial blind source separation for improved analysis of multi-omic dataAndrew E. Teschendorff0Han Jing1Dirk S. Paul2Joni Virta3Klaus Nordhausen4CAS-MPG Partner Institute for Computational Biology, CAS Key Lab of Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of SciencesCAS-MPG Partner Institute for Computational Biology, CAS Key Lab of Computational Biology, Shanghai Institute for Biological Sciences, Chinese Academy of SciencesCardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research LaboratoryUniversity of TurkuVienna University of TechnologyAbstract 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.http://link.springer.com/article/10.1186/s13059-018-1455-8Multi-omicTensorDimensional reductionIndependent component analysismQTLEpigenome-wide association study |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Andrew E. Teschendorff Han Jing Dirk S. Paul Joni Virta Klaus Nordhausen |
spellingShingle |
Andrew E. Teschendorff Han Jing Dirk S. Paul Joni Virta Klaus Nordhausen Tensorial blind source separation for improved analysis of multi-omic data Genome Biology Multi-omic Tensor Dimensional reduction Independent component analysis mQTL Epigenome-wide association study |
author_facet |
Andrew E. Teschendorff Han Jing Dirk S. Paul Joni Virta Klaus Nordhausen |
author_sort |
Andrew E. Teschendorff |
title |
Tensorial blind source separation for improved analysis of multi-omic data |
title_short |
Tensorial blind source separation for improved analysis of multi-omic data |
title_full |
Tensorial blind source separation for improved analysis of multi-omic data |
title_fullStr |
Tensorial blind source separation for improved analysis of multi-omic data |
title_full_unstemmed |
Tensorial blind source separation for improved analysis of multi-omic data |
title_sort |
tensorial blind source separation for improved analysis of multi-omic data |
publisher |
BMC |
series |
Genome Biology |
issn |
1474-760X |
publishDate |
2018-06-01 |
description |
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. |
topic |
Multi-omic Tensor Dimensional reduction Independent component analysis mQTL Epigenome-wide association study |
url |
http://link.springer.com/article/10.1186/s13059-018-1455-8 |
work_keys_str_mv |
AT andreweteschendorff tensorialblindsourceseparationforimprovedanalysisofmultiomicdata AT hanjing tensorialblindsourceseparationforimprovedanalysisofmultiomicdata AT dirkspaul tensorialblindsourceseparationforimprovedanalysisofmultiomicdata AT jonivirta tensorialblindsourceseparationforimprovedanalysisofmultiomicdata AT klausnordhausen tensorialblindsourceseparationforimprovedanalysisofmultiomicdata |
_version_ |
1725364181603975168 |