hierBipartite - Bipartite Graph-Based Hierarchical Clustering
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)
<doi:10.2202/1544-6115.1638> 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.