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

研究成果: Article同行評審

1 引文 斯高帕斯(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.

頁(從 - 到)2259-2265
出版狀態Published - 2021 8月 15

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 生物化學
  • 分子生物學
  • 電腦科學應用
  • 計算機理論與數學
  • 計算數學


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