Package: M3C
Title: Monte Carlo Reference-based Consensus Clustering
Version: 1.4.1
Authors@R: person("Christopher", "John", email = "chris.r.john86@gmail.com", role = c("aut", "cre"))
Description: 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.
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, Rtsne,
        survival
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
RoxygenNote: 6.0.1
biocViews: ImmunoOncology, Clustering, GeneExpression, Transcription,
        RNASeq, Sequencing
git_url: https://git.bioconductor.org/packages/M3C
git_branch: RELEASE_3_8
git_last_commit: 4b3f65a
git_last_commit_date: 2019-01-04
Date/Publication: 2019-01-04
NeedsCompilation: no
Packaged: 2019-01-05 04:41:02 UTC; biocbuild
Author: Christopher John [aut, cre]
Maintainer: Christopher John <chris.r.john86@gmail.com>
Built: R 3.5.2; ; 2019-01-05 12:19:54 UTC; windows
