BioConductor

M3C 1.2.0

Monte Carlo Consensus Clustering

Released May 1, 2018 by Christopher John

Dependencies

NMF 0.21.0 dendextend 1.8.0 ggplot2 3.0.0 cluster 2.0.7-1 doSNOW 1.0.16 sigclust 1.1.0 matrixcalc 1.0-3 RColorBrewer 1.1-2 foreach 1.4.4 doParallel 1.0.11 Matrix 1.2-14

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.

Source

Release History

• 1.2.0
• 1.1.7
• 1.1.6
• 1.1.5
• 1.1.4
• 1.1.2
• 1.1.1