Simultaneous Integration of Multi-omics Data Improves the Identification of Cancer Driver Modules

Dana Silverbush*, Simona Cristea, Gali Yanovich-Arad, Tamar Geiger, Niko Beerenwinkel, Roded Sharan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

44 Scopus citations

Abstract

Integrating different data types to answer biological questions is a challenging problem, which can, however, provide stronger insights than using each dataset separately. ModulOmics is a statistical framework to integrate multiple omics data types and various statistical tests into one probabilistic model, with the aim of identifying functionally connected modules. It simultaneously (rather than sequentially)optimizes all tests and efficiently searches the large candidates space with a two-step optimization procedure. Across cancer types, ModulOmics identifies key modules representing cancer-related mechanisms.

Original languageEnglish
Pages (from-to)456-466.e5
JournalCell Systems
Volume8
Issue number5
DOIs
StatePublished - 22 May 2019

Funding

FundersFunder number
Israeli Ministry of Science, Technology
Edmond J. Safra Center for Ethics, Harvard University
Engineering Research Centers
European Research Council609883
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungP2EZP2_175139
Tel Aviv University

    Keywords

    • cancer
    • cancer drivers
    • cancer pathways
    • data integration
    • driver modules
    • integer linear programming
    • mutual exclusivity
    • simultaneous optimization

    Fingerprint

    Dive into the research topics of 'Simultaneous Integration of Multi-omics Data Improves the Identification of Cancer Driver Modules'. Together they form a unique fingerprint.

    Cite this