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

doi:10.1016/j.cels.2019.04.005

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 journal › Article › peer-review

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 language	English
Pages (from-to)	456-466.e5
Journal	Cell Systems
Volume	8
Issue number	5
DOIs	https://doi.org/10.1016/j.cels.2019.04.005
State	Published - 22 May 2019

Keywords

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

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.cels.2019.04.005

Cite this

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title = "Simultaneous Integration of Multi-omics Data Improves the Identification of Cancer Driver Modules",

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.",

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AU - Yanovich-Arad, Gali

AU - Geiger, Tamar

AU - Beerenwinkel, Niko

AU - Sharan, Roded

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AB - 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.

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KW - cancer drivers

KW - cancer pathways

KW - data integration

KW - driver modules

KW - integer linear programming

KW - mutual exclusivity

KW - simultaneous optimization

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

Abstract

Keywords

UN SDGs

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