DOI: 10.18129/B9.bioc.M3C    

Monte Carlo Reference-based Consensus Clustering

Bioconductor version: Release (3.8)

Genome-wide data is used to stratify large complex datasets into classes using class discovery algorithms. A widely applied technique is consensus clustering, however; the approach is prone to overfitting and false positives. These issues arise from not considering reference distributions while selecting the number of classes (K). As a solution, we developed Monte Carlo reference-based consensus clustering (M3C). M3C uses a multi-core enabled Monte Carlo simulation to generate null distributions along the range of K which are used to select its value. Using a reference, that maintains the correlation structure of the input features, eliminates the limitations of consensus clustering. M3C uses the Relative Cluster Stability Index (RCSI) and p values to decide on the value of K and reject the null hypothesis, K=1. M3C can also quantify structural relationships between clusters, and uses spectral clustering to deal with non-Gaussian and complex structures. M3C can automatically analyse biological or clinical data with respect to the discovered classes.

Author: Christopher John [aut, cre]

Maintainer: Christopher John <chris.r.john86 at gmail.com>

Citation (from within R, enter citation("M3C")):


To install this package, start R (version "3.5") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
BiocManager::install("M3C", version = "3.8")

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biocViews Clustering, GeneExpression, RNASeq, Sequencing, Software, Transcription
Version 1.4.0
License AGPL-3
Depends R (>= 3.4.0)
Imports ggplot2, Matrix, doSNOW, NMF, RColorBrewer, cluster, parallel, foreach, doParallel, matrixcalc, dendextend, sigclust, Rtsne, survival
Suggests knitr, rmarkdown
Depends On Me
Imports Me
Suggests Me
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Source Package M3C_1.4.0.tar.gz
Windows Binary M3C_1.4.0.zip
Mac OS X 10.11 (El Capitan) M3C_1.4.0.tgz
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Package Short Url http://bioconductor.org/packages/M3C/
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