README.md
In M3C: Monte Carlo Reference-based Consensus Clustering

M3C: Monte Carlo Reference-based Consensus Clustering

M3C is a consensus clustering algorithm that improves performance by eliminating overestimation of K and can test the null hypothesis K=1.

Details:

-M3C calculates the consensus rate, a measure of stability of the co-clustering of samples, for all samples for every K -Either the PAC score or entropy can be used to quantify how stable the consensus matrix is -Generation of null models using a multi-core Monte Carlo simulation -Reference generation preserves feature-feature correlation structure of data -Using the reference distributions the Relative Cluster Stability Index (RCSI) and empirical p values are used to select K and reject the null, K=1 -Extrapolated p values are calculated by fitting normal or beta distributions -A second method is included for faster results that uses a penalty term, called regularised consensus clustering -Automatic re ordering of expression matrix and annotation data to help user do their analysis faster -User friendly PCA, tSNE, and UMAP functions -All plotting code using ggplot2 for publication quality outputs

Usage:

res <- M3C(mydata)