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ClusterSignificance
This package is for version 3.6 of Bioconductor; for the stable, up-to-date release version, see ClusterSignificance.
The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data
Bioconductor version: 3.6
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.
Author: Jason T. Serviss and Jesper R. Gadin
Maintainer: Jason T Serviss <jason.serviss at ki.se>
citation("ClusterSignificance")):
Installation
To install this package, start R (version "3.4") and enter:
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ClusterSignificance")
For older versions of R, please refer to the appropriate Bioconductor release.
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Follow Installation instructions to use this package in your R session.