Tensorial Blind Source Separation for Improved Analysis of Multi-Omic Data

Research output: Contribution to journalArticle


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

Research units

  • University of Chinese Academy of Sciences
  • University of Cambridge
  • Vienna University of Technology
  • University of Turku
  • Chinese Academy of Sciences
  • University College London


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.


Original languageEnglish
Article number76
Pages (from-to)1-18
Issue number76
Publication statusPublished - 2018
MoE publication typeA1 Journal article-refereed

    Research areas

  • Multi-omic, Tensor, Dimensional reduction, Independent component analysis, mQTL, Epigenome-wide association study, Cancer

ID: 30997695