DOI: 10.18129/B9.bioc.M3C    

This package is for version 3.7 of Bioconductor; for the stable, up-to-date release version, see M3C.

Monte Carlo Consensus Clustering

Bioconductor version: 3.7

Genome-wide data is used to stratify patients into classes using class discovery algorithms. However, we have observed systematic bias present in current state-of-the-art methods. This arises from not considering reference distributions while selecting the number of classes (K). As a solution, we developed a consensus clustering-based algorithm with a hypothesis testing framework called Monte Carlo 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 calculate p values to select its value. P values beyond the limits of the simulation are estimated using a beta distribution. M3C can quantify structural relationships between clusters and uses spectral clustering to deal with non-gaussian and imbalanced structures.

Author: Christopher John [aut, cre]

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

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biocViews Clustering, GeneExpression, RNASeq, Sequencing, Software, Transcription
Version 1.2.0
License AGPL-3
Depends R (>= 3.4.0)
Imports ggplot2, Matrix, doSNOW, NMF, RColorBrewer, cluster, parallel, foreach, doParallel, matrixcalc, dendextend, sigclust
Suggests knitr, rmarkdown
Depends On Me
Imports Me
Suggests Me
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