In CalvinTChi/hierBipartite: Bipartite Graph-Based Hierarchical Clustering

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hierBipartite

Bipartite Graph-based Hierarchical Clustering

Author: Calvin Chi

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About

Bipartite graph-based hierarchical clustering performs hierarchical clustering of groups of samples based on association patterns between two sets of variables. It is developed for pharmacogenomic datasets and datasets sharing the same data structure. In the context of pharmacogenomic datasets, the samples are cell lines, and the two sets of variables are typically expression levels and drug sensitivity values. For this method, sparse canonical correlation analysis from Lee, W., Lee, D., Lee, Y. and Pawitan, Y. (2011) is first applied to extract association patterns for each group of samples. Then, a nuclear norm-based dissimilarity measure is used to construct a dissimilarity matrix between groups based on the extracted associations. Finally, hierarchical clustering is applied.

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Installation

For standard use, we recommend installing the package from CRAN via

install.packages("hierBipartite")

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Example

Minimially sufficient example of using hierBipartite. Please refer to the vignette for detailed usage.

library(hierBipartite)
data(ctrp2)

groups = ctrp2$groups
X = ctrp2$X
Y = ctrp2$Y

result = hierBipartite(X, Y, groups)

CalvinTChi/hierBipartite documentation built on March 10, 2021, 11:25 p.m.