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. 2015 Dec 15;31(24):4032-4.
doi: 10.1093/bioinformatics/btv499. Epub 2015 Sep 2.

DIGGIT: a Bioconductor package to infer genetic variants driving cellular phenotypes

Mariano J Alvarez  1 , James C Chen  2 , Andrea Califano  1
Affiliations

DIGGIT: a Bioconductor package to infer genetic variants driving cellular phenotypes

Mariano J Alvarez et al. Bioinformatics. .

Abstract

Identification of driver mutations in human diseases is often limited by cohort size and availability of appropriate statistical models. We propose a method for the systematic discovery of genetic alterations that are causal determinants of disease, by prioritizing genes upstream of functional disease drivers, within regulatory networks inferred de novo from experimental data. Here we present the implementation of Driver-gene Inference by Genetical-Genomic Information Theory as an R-system package.

Availability and implementation: The diggit package is freely available under the GPL-2 license from Bioconductor (http://www.bioconductor.org).

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Figures

Fig. 1.
Fig. 1.
(A) Scatterplot for KLHL9 CNV vs. mRNA expression. Spearman correlation and MI p-values are indicated on top of the figure. (B) Heatmap showing the association (-log10(p)) between genes affected by genetic alterations (rows) and STAT3 inferred protein activity, while conditioning on each of the genetically altered genes (columns). The rightmost column indicates the weakest association for each gene
See this image and copyright information in PMC

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