RT Journal Article
SR Electronic
T1 Tensorial blind source separation for improved analysis of multi-omic data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 300277
DO 10.1101/300277
A1 Teschendorff, Andrew E
A1 Han, Jing
A1 Paul, Dirk S
A1 Virta, Joni
A1 Nordhausen, Klaus
YR 2018
UL http://biorxiv.org/content/early/2018/05/11/300277.abstract
AB There is an increased need for integrative analyses of multi-omic data. Although several algorithms for analysing multi-omic data exist, no study has yet performed a detailed comparison of these methods in biologically relevant contexts. Here we benchmark a novel tensorial independent component analysis (tICA) algorithm against current state-of-the-art methods. Using simulated and real multi-omic data, we find that tICA outperforms established methods in identifying biological sources of data variation at a significantly reduced computational cost. Using two independent multi cell-type EWAS, we further demonstrate how tICA can identify, in the absence of genotype information, mQTLs at a higher sensitivity than competing multi-way algorithms. We validate mQTLs found with tICA in an independent set, and demonstrate that approximately 75% of mQTLs are independent of blood cell subtype. In an application to multi-omic cancer data, tICA identifies many gene modules whose expression variation across tumors is driven by copy number or DNA methylation changes, but whose deregulation relative to the normal state is independent such alterations, an important finding that we confirm by direct analysis of individual data types. In summary, tICA is a powerful novel algorithm for decomposing multi-omic data, which will be of great value to the research community.