Probabilistic dependency analysis for functional genomics. In particular, tools for integrative screening of genome-wide RNA and DNA profiling data for cancer gene discovery and functional analysis of chromosomal aberrations Lahti et al. MLSP 2009.
The package implements probabilistic models for integrative analysis of mRNA expression levels with DNA copy number (aCGH) measurements to discover functionally active chromosomal alterations. The algorithms can be used to discover functionally altered chromosomal regions and to visualize the affected genes and samples. The algorithms can be applied also to other types of biomedical data, including epigenetic modifications, SNPs, alternative splicing and transcription factor binding, or in other application fields. By investigating dependencies between different functional layers of the genome it is possible to discover mechanisms and interactions that are not seen in the individual measurement sources. For instance, integration of gene expression and DNA copy number can reveal cancer-associated chromosomal regions and associated genes with potential diagnostic, prognostic and clinical impact.
The methods are based on latent variable models including probabilistic canonical correlation analysis and related extensions, implemented in the dmt package. Probabilistic formulation deals rigorously with uncertainty associated with small sample sizes common in biomedical studies and provides tools to guide dependency modeling through Bayesian priors.
This is the development version of the package. For a stable release version, see Bioconductor. For further details and examples, see the package vignette.