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multiClust

DOI: 10.18129/B9.bioc.multiClust    

This is the development version of multiClust; for the stable release version, see multiClust.

multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles

Bioconductor version: Development (3.7)

Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies.

Author: Nathan Lawlor [aut, cre], Peiyong Guan [aut], Alec Fabbri [aut], Krish Karuturi [aut], Joshy George [aut]

Maintainer: Nathan Lawlor <nathan.lawlor03 at gmail.com>

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

Installation

To install this package, start R and enter:

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

Documentation

HTML R Script A Guide to multiClust
PDF   Reference Manual
Text   NEWS

Details

biocViews Clustering, FeatureExtraction, GeneExpression, Software, Survival
Version 1.9.0
In Bioconductor since BioC 3.3 (R-3.3) (1.5 years)
License GPL (>= 2)
Depends
Imports mclust, ctc, survival, cluster, dendextend, amap, graphics, grDevices
LinkingTo
Suggests knitr, gplots, RUnit, BiocGenerics, preprocessCore, Biobase, GEOquery
SystemRequirements
Enhances
URL
Depends On Me
Imports Me
Suggests Me
Build Report  

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Source Package multiClust_1.9.0.tar.gz
Windows Binary multiClust_1.8.1.zip
Mac OS X 10.11 (El Capitan) multiClust_1.9.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/multiClust
Package Short Url http://bioconductor.org/packages/multiClust/
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