@article{chang_yang_lu_huang_huang_hung_miecznikowski_lu_tzeng_2021, title={Gene-set integrative analysis of multi-omics data using tensor-based association test}, volume={37}, ISSN={["1460-2059"]}, DOI={10.1093/bioinformatics/btab125}, abstractNote={Abstract
               
                  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.
               }, number={16}, journal={BIOINFORMATICS}, author={Chang, Sheng-Mao and Yang, Meng and Lu, Wenbin and Huang, Yu-Jyun and Huang, Yueyang and Hung, Hung and Miecznikowski, Jeffrey C. and Lu, Tzu-Pin and Tzeng, Jung-Ying}, year={2021}, month={Aug}, pages={2259–2265} }
 @article{xu_chi_yang_lange_2018, title={A majorization-minimization algorithm for split feasibility problems}, volume={71}, ISSN={["1573-2894"]}, url={https://doi.org/10.1007/s10589-018-0025-z}, DOI={10.1007/s10589-018-0025-z}, number={3}, journal={COMPUTATIONAL OPTIMIZATION AND APPLICATIONS}, publisher={Springer Science and Business Media LLC}, author={Xu, Jason and Chi, Eric C. and Yang, Meng and Lange, Kenneth}, year={2018}, month={Dec}, pages={795–828} }
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