To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("MCbiclust")

In most cases, you don't need to download the package archive at all.

MCbiclust

 

   

Massive correlating biclusters for gene expression data and associated methods

Bioconductor version: Release (3.5)

Custom made algorithm and associated methods for finding, visualising and analysing biclusters in large gene expression data sets. Algorithm is based on with a supplied gene set of size n, finding the maximum strength correlation matrix containing m samples from the data set.

Author: Robert Bentham

Maintainer: Robert Bentham <robert.bentham.11 at ucl.ac.uk>

Citation (from within R, enter citation("MCbiclust")):

Installation

To install this package, start R and enter:

## try http:// if https:// URLs are not supported
source("https://bioconductor.org/biocLite.R")
biocLite("MCbiclust")

Documentation

HTML R Script Introduction to MCbiclust
PDF   Reference Manual

Details

biocViews Clustering, GeneExpression, Microarray, RNASeq, Software, StatisticalMethod
Version 1.0.1
In Bioconductor since BioC 3.5 (R-3.4) (< 6 months)
License GPL-2
Depends R (>= 3.4)
Imports BiocParallel, graphics, utils, stats, AnnotationDbi, GO.db, org.Hs.eg.db, GGally, ggplot2, scales, cluster
LinkingTo
Suggests gplots, knitr, rmarkdown, BiocStyle, gProfileR, MASS, dplyr, pander, devtools, testthat
SystemRequirements
Enhances
URL
Depends On Me
Imports Me
Suggests Me
Build Report  

Package Archives

Follow Installation instructions to use this package in your R session.

Package Source MCbiclust_1.0.1.tar.gz
Windows Binary MCbiclust_1.0.1.zip
Mac OS X 10.11 (El Capitan) MCbiclust_1.0.1.tgz
Subversion source (username/password: readonly)
Git source https://github.com/Bioconductor-mirror/MCbiclust/tree/release-3.5
Package Short Url http://bioconductor.org/packages/MCbiclust/
Package Downloads Report Download Stats

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