Gene-set integrative analysis of multi-omics data using tensor-based association test
| Autor: | Yueyang Huang, Tzu-Pin Lu, Jeffrey C. Miecznikowski, Yu-Jyun Huang, Hung Hung, Wenbin Lu, Meng Yang, Sheng Mao Chang, Jung-Ying Tzeng |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Computer science media_common.quotation_subject Machine learning computer.software_genre 01 natural sciences Biochemistry Set (abstract data type) 010104 statistics & probability 03 medical and health sciences Tensor (intrinsic definition) 0101 mathematics Function (engineering) Molecular Biology 030304 developmental biology media_common Disease gene Structure (mathematical logic) 0303 health sciences business.industry Subject (documents) Omics Original Papers Computer Science Applications Computational Mathematics Computational Theory and Mathematics Multi omics Artificial intelligence business computer |
| Zdroj: | Bioinformatics |
| ISSN: | 1367-4811 1367-4803 |
| DOI: | 10.1093/bioinformatics/btab125 |
| Popis: | 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. Availability and implementation R function and instruction are available from the authors’ website: https://www4.stat.ncsu.edu/~jytzeng/Software/TR.omics/TRinstruction.pdf. Supplementary information Supplementary data are available at Bioinformatics online. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|