Package: M3C
Title: Monte Carlo Consensus Clustering
Version: 1.2.0
Authors@R: person("Christopher", "John", email = "chris.r.john86@gmail.com", role = c("aut", "cre"))
Description: 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.
Depends: R (>= 3.4.0)
License: AGPL-3
Encoding: UTF-8
LazyData: true
Imports: ggplot2, Matrix, doSNOW, NMF, RColorBrewer, cluster, parallel,
        foreach, doParallel, matrixcalc, dendextend, sigclust
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
RoxygenNote: 6.0.1
biocViews: Clustering, GeneExpression, Transcription, RNASeq,
        Sequencing
NeedsCompilation: no
Packaged: 2018-05-01 04:16:00 UTC; biocbuild
Author: Christopher John [aut, cre]
Maintainer: Christopher John <chris.r.john86@gmail.com>
Built: R 3.5.0; ; 2018-05-01 12:17:48 UTC; windows
