ClusterSignificance: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data
Version 1.8.2

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

AuthorJason T. Serviss [aut, cre], Jesper R. Gadin [aut]
Bioconductor views Classification Clustering PrincipalComponent StatisticalMethod
Date of publication2018-07-16
MaintainerJason T Serviss <[email protected]>
LicenseGPL-3
Version1.8.2
URL https://github.com/jasonserviss/ClusterSignificance/
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("ClusterSignificance")

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ClusterSignificance documentation built on July 19, 2018, 2 a.m.