Dimensionality reduction of longitudinal’omics data using modern tensor factorizations

Uria Mor, Yotam Cohen, Rafael Valdés-Mas, Denise Kviatcovsky, Eran Elinav, Haim Avron*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Longitudinal’omics analytical methods are extensively used in the field of evolving precision medicine, by enabling ‘big data’ recording and high-resolution interpretation of complex datasets, driven by individual variations in response to perturbations such as disease pathogenesis, medical treatment or changes in lifestyle. However, inherent technical limitations in biomedical studies often result in the generation of feature-rich and sample-limited datasets. Analyzing such data using conventional modalities often proves to be challenging since the repeated, high-dimensional measurements overload the outlook with inconsequential variations that must be filtered from the data in order to find the true, biologically relevant signal. Tensor methods for the analysis and meaningful representation of multi-way data may prove useful to the biological research community by their advertised ability to tackle this challenge. In this study, we present TCAM—a new unsupervised tensor factorization method for the analysis of multi-way data. Building on top of cutting-edge developments in the field of tensor-tensor algebra, we characterize the unique mathematical properties of our method, namely, 1) preservation of geometric and statistical traits of the data, which enables uncovering information beyond the inter-individual variation that often takes-over the focus, especially in human studies. 2) Natural and straightforward out-of-sample extension, making TCAM amenable for integration in machine learning workflows. A series of re-analyses of real-world, human experimental datasets showcase these theoretical properties, while providing empirical confirmation of TCAM’s utility in the analysis of longitudinal’omics data.

Original languageEnglish
Article numbere1010212
JournalPLoS Computational Biology
Issue number7
StatePublished - Jul 2022


FundersFunder number
Ben B. and Joyce E. Eisenberg Foundation
Daniel Morris Trust
Deutsch-Israelische Projektkooperation
European Research Council, Israel Science Foundation
Hanna and Dr. Ludwik Wallach Cancer Research Fund
IDSA Foundation
Israel Ministry of Science and Technology, Israel Ministry of Health
Park Avenue Charitable Fund
Pearl Welinsky Merlo Scientific Progress Research Fund
Weizmann Data Science Research Center
Howard Hughes Medical Institute
Bill and Melinda Gates Foundation
International Business Machines Corporation
Leona M. and Harry B. Helmsley Charitable Trust
Canadian Institute for Advanced Research
Wellcome Trust
European Crohn's and Colitis Organisation
Wolfson Foundation
United States-Israel Binational Science Foundation
Else Kröner-Fresenius-Stiftung
Garvan Institute of Medical Research
Israel Science Foundation
Council for Higher Education
Wolfson Family Charitable Trust
Helmholtz Association


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