Enter the Matrix: Factorization Uncovers Knowledge from Omics

Genevieve Stein-O’Brien

doi:10.1016/j.tig.2018.07.003

Enter the Matrix: Factorization Uncovers Knowledge from Omics

2018-10-01

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Genevieve Stein-O’Brien

DOI：10.1016/j.tig.2018.07.003

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Omics data contain signals from the molecular, physical, and kinetic inter- and intracellular interactions that control biological systems. Matrix factorization (MF) techniquescan 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:

INDEPENDENT COMPONENT ANALYSIS

SINGULAR-VALUE DECOMPOSITION

SET ENRICHMENT ANALYSIS

GENE-EXPRESSION DATA

NONNEGATIVE MATRIX

POPULATION-STRUCTURE

MICROARRAY DATA

SIGNATURES

INFERENCE

IDENTIFICATION

Enter the Matrix: Factorization Uncovers Knowledge from Omics

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