Enter the matrix: Factorization uncovers knowledge from omics Names/Affiliations

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

doi:10.1101/196915

Enter the matrix: Factorization uncovers knowledge from omics Names/Affiliations

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

School of Medicine

Research output: Contribution to journal › Article › peer-review

Abstract

Omics data contains signal from the molecular, physical, and kinetic inter- and intra-cellular interactions that control biological systems. Matrix factorization 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 topics ranging from pathway discovery to time course analysis. We review exemplary applications of matrix factorization for systems-level analyses. We discuss appropriate application of these methods, their limitations, and focus on analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with matrix factorization 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 language	English (US)
Journal	Unknown Journal
DOIs	https://doi.org/10.1101/196915
State	Published - Oct 2 2017

Keywords

dimension reduction
genomics
integrated analyses
matrix factorization
single cell
unsupervised learning

ASJC Scopus subject areas

Biochemistry, Genetics and Molecular Biology(all)
Agricultural and Biological Sciences(all)
Immunology and Microbiology(all)
Neuroscience(all)
Pharmacology, Toxicology and Pharmaceutics(all)

Access to Document

10.1101/196915

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Enter the matrix : Factorization uncovers knowledge from omics Names/Affiliations. / Stein-O’Brien, Genevieve L.; Arora, Raman; Culhane, Aedin C.; Favorov, Alexander V.; Garmire, Lana X.; Greene, Casey S.; Goff, Loyal A.; Li, Yifeng; Ngom, Aloune; Ochs, Michael F.; Xu, Yanxun; Fertig, Elana J.

In: Unknown Journal, 02.10.2017.

Research output: Contribution to journal › Article › peer-review

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abstract = "Omics data contains signal from the molecular, physical, and kinetic inter- and intra-cellular interactions that control biological systems. Matrix factorization 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 topics ranging from pathway discovery to time course analysis. We review exemplary applications of matrix factorization for systems-level analyses. We discuss appropriate application of these methods, their limitations, and focus on analysis of results to facilitate optimal biological interpretation. The inference of biologically relevant features with matrix factorization 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.",

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AU - Ngom, Aloune

AU - Ochs, Michael F.

AU - Xu, Yanxun

AU - Fertig, Elana J.

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