Gene-set integrative analysis of multi-omics data using tensor-based association test

Sheng Mao Chang, Meng Yang, Wenbin Lu, Yu Jyun Huang, Yueyang Huang, Hung Hung, Jeffrey C. Miecznikowski, Tzu Pin Lu, Jung Ying Tzeng

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Motivation: Facilitated by technological advances and the decrease in costs, it is feasible to gather subject data from several omics platforms. Each platform assesses different molecular events, and the challenge lies in efficiently analyzing these data to discover novel disease genes or mechanisms. A common strategy is to regress the outcomes on all omics variables in a gene set. However, this approach suffers from problems associated with high-dimensional inference. Results: We introduce a tensor-based framework for variable-wise inference in multi-omics analysis. By accounting for the matrix structure of an individual's multi-omics data, the proposed tensor methods incorporate the relationship among omics effects, reduce the number of parameters, and boost the modeling efficiency. We derive the variable-specific tensor test and enhance computational efficiency of tensor modeling. Using simulations and data applications on the Cancer Cell Line Encyclopedia (CCLE), we demonstrate our method performs favorably over baseline methods and will be useful for gaining biological insights in multi-omics analysis. VC The Author(s) 2021. Published by Oxford University Press. All rights reserved.

Original languageEnglish
Pages (from-to)2259-2265
Number of pages7
Issue number16
Publication statusPublished - 2021 Aug 15

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics


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