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M3C

Monte Carlo Reference-based Consensus Clustering

M3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overestimation of K and can reject the null hypothesis K=1.

M3C

M3C: Monte Carlo Reference-based Consensus Clustering

clustersim: A cluster simulator for testing clustering algorithms

GBM clinical annotation data

featurefilter: A function for filtering features

GBM expression data

pca

pca: A principal component analysis function

tsne: A t-SNE function

umap: A umap function

Monte Carlo Reference-based Consensus Clustering

v1.12.0

AGPL-3

Authors

Christopher John, David Watson

Initial release

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