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DOI: 10.18129/B9.bioc.M3C    

This is the development version of M3C; for the stable release version, see M3C.

Monte Carlo Consensus Clustering

Bioconductor version: Development (3.7)

A central task in genomic data analyses is class discovery which is accomplished through clustering. However, an unresolved problem with current clustering algorithms is they do not not have a hypothesis testing framework that derives p values for each value of the number of clusters (K). To solve this, we developed a clustering tool with a hypothesis testing framework that uses consensus clustering called Monte Carlo Consensus Clustering (M3C). M3C use a multi-core enabled Monte Carlo simulation to generate a distribution of stability scores for each value of K using null datasets with the same gene-gene correlation structure as the real one. These distributions are used to derive p values and a beta distribution is fitted to the data to cheaply estimate p values beyond the limits of the simulation. M3C improves accuracy, allows rejection of the null hypothesis, removes systematic bias, and uses p values to make class number decisions.

Author: Christopher John [aut, cre]

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

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biocViews Clustering, GeneExpression, RNASeq, Sequencing, Software, Transcription
Version 1.1.1
License AGPL-3
Depends R (>= 3.4.0)
Imports ggplot2, Matrix, doSNOW, NMF, RColorBrewer, cluster, parallel, foreach, doParallel, matrixcalc
Suggests knitr, rmarkdown
Depends On Me
Imports Me
Suggests Me
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