Dimension Reduction for the Integrative Analysis of Multilevel Omics Data

Gerhard Thallinger, Bettina Pucher, Natascha Fladischer, Oana Alina Zeleznik

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

Abstract

Biological systems are increasingly studied at multiple omics levels simultaneously. Sequential analysis of the individual omics levels does not take the interconnected nature of the data into account. Integrative analysis of the multilevel data using dimension reduction techniques facilitate the identification of a larger number and more relevant features than sequential methods. We apply principal component-,
correspondence- and multiple co-inertia analysis to transcriptome and proteome data from 57 cancer cell lines and show that MCIA allows identification of a more complete set of relevant features and more accurate classification of the cancer types.
Original languageEnglish
Title of host publicationProceedings of SIAM ALA 2018
Publication statusPublished - May 2018

Fields of Expertise

  • Human- & Biotechnology
  • Information, Communication & Computing

Treatment code (Nähere Zuordnung)

  • Basic - Fundamental (Grundlagenforschung)

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  • Cite this

    Thallinger, G., Pucher, B., Fladischer, N., & Zeleznik, O. A. (2018). Dimension Reduction for the Integrative Analysis of Multilevel Omics Data. In Proceedings of SIAM ALA 2018