scale_features

<p>an n x p matrix (gene expression or drug sensitivity)</p>

<p>Only scales features (columns) with positive variance</p>

internal


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.

Calvin Chi

hierBipartite

Bipartite Graph-Based Hierarchical Clustering

Woojoo Lee

Donghwan Lee

Youngjo Lee

Yudi Pawitan

scale_features function

Only scales features (columns) with positive variance — scale_features

Only scales features (columns) with positive variance

scale_features: Only scales features (columns) with positive variance

Description

Usage

Arguments

Value