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Trends Genet. 2018 Oct;34(10):790-805. doi: 10.1016/j.tig.2018.07.003. Epub 2018 Aug 22.

Enter the Matrix: Factorization Uncovers Knowledge from Omics.

Author information

1
Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA; Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
2
Department of Computer Science, Institute for Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, MD, USA.
3
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA.
4
Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA; Vavilov Institute of General Genetics, Moscow, Russia.
5
University of Hawaii Cancer Center, Honolulu, HI, USA.
6
Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, PA, USA; Childhood Cancer Data Lab, Alex's Lemonade Stand Foundation, PA, USA.
7
Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA; McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA.
8
Digital Technologies Research Centre, National Research Council of Canada, Ottawa, ON, Canada.
9
School of Computer Science, University of Windsor, Windsor, ON, Canada.
10
Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, USA.
11
Department of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.
12
Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA. Electronic address: ejfertig@jhmi.edu.

Abstract

Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniques can reveal low-dimensional structure from high-dimensional data that reflect these interactions. These techniques can uncover new biological knowledge from diverse high-throughput omics data in applications ranging from pathway discovery to timecourse analysis. We review exemplary applications of MF for systems-level analyses. We discuss appropriate applications of these methods, their limitations, and focus on the analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with MF enables discovery from high-throughput data beyond the limits of current biological knowledge - answering questions from high-dimensional data that we have not yet thought to ask.

KEYWORDS:

deconvolution; dimension reduction; genomics; matrix factorization; single cell; unsupervised learning

PMID:
30143323
PMCID:
PMC6309559
[Available on 2019-10-01]
DOI:
10.1016/j.tig.2018.07.003
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