To install this package, start R and enter:

## try http:// if https:// URLs are not supported

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



This package is for version 3.3 of Bioconductor; for the stable, up-to-date release version, see multiClust.

A collection of gene feature selection and clustering analysis algorithms

Bioconductor version: 3.3

Whole transcriptomic profiles are useful for studying the expression levels of thousands of genes across samples. Clustering algorithms are used to identify patterns in these profiles to determine clinically relevant subgroups. Feature selection is a critical 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 the appropriate methods is difficult as recent work demonstrates that no method is the clear winner. Hence, we present an R-package called `multiClust` that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. In addition, using multiClust, we present the merit of gene selection and clustering methods in the context of clinical relevance of clustering, specifically clinical outcome. Our integrative R- package contains: 1. A function to read in gene expression data and format appropriately for analysis in R. 2. Four different ways to select the number of genes a. Fixed b. Percent c. Poly d. GMM 3. Four gene ranking options that order genes based on different statistical criteria a. CV_Rank b. CV_Guided c. SD_Rank d. Poly 4. Two ways to determine the cluster number a. Fixed b. Gap Statistic 5. Two clustering algorithms a. Hierarchical clustering b. K-means clustering 6. A function to calculate average gene expression in each sample cluster 7. A function to correlate sample clusters with clinical outcome Order of Function use: 1. input_file, a function to read-in the gene expression file and assign gene probe names as the rownames. 2. number_probes, a function to determine the number of probes to select for in the gene feature selection process. 3. probe_ranking, a function to select for gene probes using one of the available gene probe ranking options. 4. number_clusters, a function to determine the number of clusters to be used to cluster genes and samples. 5. cluster_analysis, a function to perform Kmeans or Hierarchical clustering analysis of the selected gene expression data. 6. avg_probe_exp, a function to produce a matrix containing the average expression of each gene probe within each sample cluster. 7. surv_analysis, a function to produce Kaplan-Meier Survival Plots of selected gene expression data.

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

Maintainer: Nathan Lawlor <nathan.lawlor03 at>

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


To install this package, start R and enter:

## try http:// if https:// URLs are not supported


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


biocViews Clustering, FeatureExtraction, GeneExpression, Software, Survival
Version 1.0.2
In Bioconductor since BioC 3.3 (R-3.3) (< 6 months)
License GPL (>= 2)
Imports mclust, ctc, survival, cluster, dendextend, amap, graphics, grDevices
Suggests knitr, gplots, RUnit, BiocGenerics, preprocessCore, Biobase, GEOquery
Depends On Me
Imports Me
Suggests Me
Build Report  

Package Archives

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

Package Source multiClust_1.0.2.tar.gz
Windows Binary
Mac OS X 10.9 (Mavericks) multiClust_1.0.2.tgz
Subversion source (username/password: readonly)
Git source
Package Short Url
Package Downloads Report Download Stats

Documentation »


R / CRAN packages and documentation

Support »

Please read the posting guide. Post questions about Bioconductor to one of the following locations: