Enter the Matrix: Factorization Uncovers Knowledge from Omics

Genevieve L. Stein-O'Brien, Raman Arora, Aedin C. Culhane, Alexander V. Favorov, Lana X. Garmire, Casey S. Greene, Loyal A. Goff, Yifeng Li, Aloune Ngom, Michael F. Ochs, Yanxun Xu, Elana J. Fertig

Research output: Contribution to journalReview articlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)790-805
Number of pages16
JournalTrends in Genetics
Volume34
Issue number10
DOIs
StatePublished - Oct 2018

Keywords

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

ASJC Scopus subject areas

  • Genetics

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