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Visualise microarray and RNAseq data using gene ontology annotations

Bioconductor version: 3.1

The package contains methods to visualise the expression profile of genes from a microarray or RNA-seq experiment, and offers a supervised clustering approach 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 may be obtained from Ensembl through the biomaRt package, if not provided by the user. The default 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. P-values may be computed to assess the significance of GO term ranking. Visualisation function include gene expression profile, gene ontology-based heatmaps, and hierarchical clustering of experimental samples using gene expression data.

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, Microarray, MultipleComparison, Pathways, RNASeq, Sequencing, Software, TimeCourse, Transcription, Visualization
Version 1.2.2
In Bioconductor since BioC 3.0 (R-3.1) (1 year)
License GPL (>= 3)
Depends R (>= 3.0.2), grid, Biobase(>= 2.22.0), VennDiagram (>= 1.6.5)
Imports biomaRt(>= 2.18.0), stringr (>= 0.6.2), ggplot2 (>= 0.9.0), RColorBrewer (>= 1.0), gplots (>= 2.13.0), randomForest (>= 4.6)
Suggests RCurl (>= 1.95), BiocStyle
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