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|
|Bioconductor views||Classification Clustering PrincipalComponent StatisticalMethod|
|Maintainer||Jason T Serviss <firstname.lastname@example.org>|
|Package repository||View on Bioconductor|
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