The estimation of dimensionality in gene expression data using Nonnegative Matrix Factorization

Conor J. Kelton, Waishing Lee, Matthew Rusay, Ondrej Maxian, Elana Fertig, Michael F. Ochs

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Nonnegative matrix factorization and other decomposition methods have proven to offer significant advantages for the interpretation of genome-wide gene expression data. However, unlike analytic methods, they suffer from instability in the inferred factors or patterns as the dimensionality is changed. We present here two statistics, one mathematical and one biological, that estimate the dimensionality. We show that they provide close though not identical estimates, and that they provide strong evidence for elimination of some potential factorizations.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1642-1649
Number of pages8
ISBN (Print)9781467367981
DOIs
StatePublished - Dec 16 2015
EventIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 - Washington, United States
Duration: Nov 9 2015Nov 12 2015

Other

OtherIEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015
CountryUnited States
CityWashington
Period11/9/1511/12/15

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Health Informatics
  • Biomedical Engineering

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  • Cite this

    Kelton, C. J., Lee, W., Rusay, M., Maxian, O., Fertig, E., & Ochs, M. F. (2015). The estimation of dimensionality in gene expression data using Nonnegative Matrix Factorization. In Proceedings - 2015 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2015 (pp. 1642-1649). [7359922] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2015.7359922