The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data. The term class clusters here refers to, clusters of points representing known classes in the data. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. ClusterSignificance accomplishes this by, projecting all points onto a one dimensional line. Cluster separations are then scored and the probability of the seen separation being due to chance is evaluated using a permutation method.
Package details 


Author  Jason T. Serviss [aut, cre], Jesper R. Gadin [aut] 
Bioconductor views  Classification Clustering PrincipalComponent StatisticalMethod 
Maintainer  Jason T Serviss <[email protected]> 
License  GPL3 
Version  1.9.2 
URL  https://github.com/jasonserviss/ClusterSignificance/ 
Package repository  View on GitHub 
Installation 
Install the latest version of this package by entering the following in R:

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