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Visualise microarray and RNAseq data using gene ontology annotations

Bioconductor version: 3.0

The package contains methods to visualise the expression levels of genes from a microarray or RNA-seq experiment and offers a clustering analysis to identify GO terms enriched in genes with expression levels best clustering two or more predefined groups of samples. Annotations for the genes present in the expression dataset are obtained from Ensembl through the biomaRt package. The random forest framework is used to evaluate the ability of each gene to cluster samples according to the factor of interest. Finally, GO terms are scored by averaging the rank (alternatively, score) of their respective gene sets to cluster the samples. An ANOVA approach is also available as an alternative statistical framework.

Author: Kevin Rue-Albrecht [aut, cre], Paul A. McGettigan [ctb], Belinda Hernandez [ctb], David A. Magee [ctb], Nicolas C. Nalpas [ctb], Andrew Parnell [ctb], Stephen V. Gordon [ths], David E. MacHugh [ths]

Maintainer: Kevin Rue-Albrecht <kevin.rue at>

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biocViews Annotation, Clustering, DataRepresentation, DifferentialExpression, GO, GeneExpression, GeneSetEnrichment, Metagenomics, Microarray, MultipleComparison, Pathways, RNASeq, Sequencing, Software, TimeCourse, Transcription, Visualization
Version 1.0.1
In Bioconductor since BioC 3.0 (R-3.1)
License GPL (>= 3)
Depends R (>= 3.0.2), grid, Biobase(>= 2.22.0)
Imports biomaRt(>= 2.18.0), stringr (>= 0.6.2), ggplot2 (>= 0.9.0), RColorBrewer (>= 1.0), gplots (>= 2.13.0), VennDiagram (>= 1.6.5), randomForest (>= 4.6)
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