hierBipartite: Bipartite Graph-Based Hierarchical Clustering
Bipartite graph-based hierarchical clustering, developed for pharmacogenomic
datasets and datasets sharing the same data structure. The goal is to
construct a hierarchical clustering of groups of samples based on
association patterns between two sets of variables. 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.
| Version: |
0.0.2 |
| Imports: |
parallel, magrittr, irlba |
| Suggests: |
knitr, rmarkdown, testthat |
| Published: |
2021-02-16 |
| Author: |
Calvin Chi [aut, cre, cph],
Woojoo Lee [ctb],
Donghwan Lee [ctb],
Youngjo Lee [ctb],
Yudi Pawitan [ctb] |
| Maintainer: |
Calvin Chi <calvin.chi at berkeley.edu> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
hierBipartite results |
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