Bioconductor version: Release (3.13)
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")
):
To install this package, start R (version "4.1") and enter:
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("MCbiclust")
For older versions of R, please refer to the appropriate Bioconductor release.
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("MCbiclust")
HTML | R Script | Introduction to MCbiclust |
Reference Manual |
biocViews | Clustering, GeneExpression, ImmunoOncology, Microarray, RNASeq, Software, StatisticalMethod |
Version | 1.16.0 |
In Bioconductor since | BioC 3.5 (R-3.4) (4.5 years) |
License | GPL-2 |
Depends | R (>= 3.4) |
Imports | BiocParallel, graphics, utils, stats, AnnotationDbi, GO.db, org.Hs.eg.db, GGally, ggplot2, scales, cluster, WGCNA |
LinkingTo | |
Suggests | gplots, knitr, rmarkdown, BiocStyle, gProfileR, MASS, dplyr, pander, devtools, testthat, GSVA |
SystemRequirements | |
Enhances | |
URL | |
Depends On Me | |
Imports Me | |
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Build Report |
Follow Installation instructions to use this package in your R session.
Source Package | MCbiclust_1.16.0.tar.gz |
Windows Binary | MCbiclust_1.16.0.zip |
macOS 10.13 (High Sierra) | MCbiclust_1.16.0.tgz |
Source Repository | git clone https://git.bioconductor.org/packages/MCbiclust |
Source Repository (Developer Access) | git clone git@git.bioconductor.org:packages/MCbiclust |
Package Short Url | https://bioconductor.org/packages/MCbiclust/ |
Package Downloads Report | Download Stats |
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