Uses probabilistic integration of cancer genomics data for combined evaluation of RNA-seq gene expression and 450K array DNA methylation measurements of promoters as well as gene bodies. The method learns the specific relationships between the data types and exploits these for biomarker discovery and classification of new samples. It also explicitly models the uncertainty of both the count-based NGS data and the continuous array data measurements. This approach is specifically tailored for cancer studies where much heterogeneity is observed among tumours. The method combines graphical model formalism with non-parametric specification of probability distributions to capture the highly context-specific relationships between methylation patterns and gene expression.

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PINCAGE classification

PINCAGE specifications

Software type:
Package/Module
Restrictions to use:
None
Programming languages:
R
Stability:
Stable
Interface:
Command line interface
Operating system:
Unix/Linux, Mac OS, Windows
Computer skills:
Advanced
Maintained:
Yes

PINCAGE support

Maintainer

  • Michal Switnicki <>

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Publications

Institution(s)

Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark; Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark

Funding source(s)

This work was supported by The Danish Strategic Research Council (Innovation Fund Denmark) and the Sapere Aude program of the Danish Council for Independent Research | Medical Sciences.

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