performHierarchicalClustering: Perform Hierarchical Clustering

In brianbennettphd/goSTAG: A tool to use GO Subtrees to Tag and Annotate Genes within a set

Description

Performs a hierarchical clustering analysis on a GO enrichment matrix.

Usage

performHierarchicalClustering(enrichment_matrix, feature = "row", distance_method = "correlation", clustering_method = "average")

Arguments

enrichment_matrix	A matrix of enrichment scores. Rows correspond to GO terms and columns correspond to gene lists.
feature	A value indicating whether to cluster the rows or the columns. Acceptable options are "row" or "col".
distance_method	The distance measure to use when generating the distance matrix. If "correlation" (default), this function will use one minus the absolute value of the correlation to measure distance. Otherwise, this function will use the "dist" function to measure distance. Available options are those of the "method" argument for the "dist" function, which are currently "euclidean", "maximum", "manhattan", "canberra", "binary", or "minkowski".
clustering_method	The agglomeration method to use when performing the hierarchical clustering. Available options are those of the "method" argument for the "hclust" function, which are currently "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", or "centroid".

Value

An object of class "hclust" containing a tree produced by hierarchical clustering.

Author(s)

Brian D. Bennett
Pierre R. Bushel

References

Bennett BD and Bushel PR. goSTAG: Gene Ontology Subtrees to Tag and Annotate Genes within a set. Source Code Biol Med. 2017 Apr 13.

Examples

data( goSTAG_example_gene_lists )
go_terms <- loadGOTerms()
enrichment_matrix <- performGOEnrichment( goSTAG_example_gene_lists, go_terms )

hclust_results <- performHierarchicalClustering( enrichment_matrix )
sample_hclust_results <- performHierarchicalClustering( enrichment_matrix, feature = "col" )

brianbennettphd/goSTAG documentation built on Nov. 14, 2020, 7:21 a.m.