DOI: 10.18129/B9.bioc.M3C    

This package is for version 3.8 of Bioconductor; for the stable, up-to-date release version, see M3C.

Monte Carlo Reference-based Consensus Clustering

Bioconductor version: 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>

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biocViews Clustering, GeneExpression, ImmunoOncology, RNASeq, Sequencing, Software, Transcription
Version 1.4.1
In Bioconductor since BioC 3.6 (R-3.4) (1.5 years)
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
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