Visualization, Analysis and Adjustment of High-Dimensional Data in Respect to Sample Annotations

Collection of functions to connect the structure of the data with the information on the samples. Three types of associations are covered: 1. linear model of principal components. 2. hierarchical clustering analysis. 3. distribution of features-sample annotation associations. Additionally, the inter-relation between sample annotations can be analyzed. Simple methods are provided for the correction of batch effects and removal of principal components.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


1.5.1 by Martin Lauss, 2 years ago

Browse source code at

Authors: Martin Lauss

Documentation:   PDF Manual  

GPL (>= 2) license

Imports methods

Depends on impute, amap, gplots, MASS

See at CRAN