This is the development version of MCbiclust; for the stable release version, see MCbiclust.
Massive correlating biclusters for gene expression data and associated methodsBioconductor version: Development (3.22)
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, entercitation("MCbiclust")
): Installation
To install this package, start R (version "4.5") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# The following initializes usage of Bioc devel
BiocManager::install(version='devel')
BiocManager::install("MCbiclust")
For older versions of R, please refer to the appropriate Bioconductor release.
DocumentationTo view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("MCbiclust")
Details biocViews Clustering, GeneExpression, ImmunoOncology, Microarray, RNASeq, Software, StatisticalMethod Version 1.33.0 In Bioconductor since BioC 3.5 (R-3.4) (8 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 System Requirements URL See More Suggests gplots, knitr, rmarkdown, BiocStyle, gProfileR, MASS, dplyr, pander, devtools, testthat, GSVA Linking To Enhances Depends On Me Imports Me Suggests Me Links To Me Build Report Build Report Package Archives
Follow Installation instructions to use this package in your R session.
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