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

• Main Authors: Andrew E. Teschendorff, Han Jing, Dirk S. Paul, Joni Virta, Klaus Nordhausen
• Format: Article
• Language: English
• Published: BMC 2018-06-01
• Series: Genome Biology
• Subjects: Multi-omic; Tensor; Dimensional reduction; Independent component analysis; mQTL; Epigenome-wide association study
• Online Access: http://link.springer.com/article/10.1186/s13059-018-1455-8

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.