The package contains 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.
|Date of publication||2013-03-13 14:11:11|
|Maintainer||Martin Lauss <firstname.lastname@example.org>|
|License||GPL (>= 2)|
adjust.linearmodel: Batch adjustment using a linear model
combat: ComBat algorithm to combine batches.
confounding: Heatmap of interrelation of sample annotations
corrected.p: Correction of p-values for associations between features and...
dense.plot: Density plots of feature associations in observed and...
feature.assoc: Associations of the features to a sample annotation in...
hca.plot: Dendrogram with according sample annotations
hca.test: Tests for annotation differences among sample clusters
kill.pc: Removes principal components from a data matrix
prince: Linear models of prinicipal conponents dependent on sample...
prince.plot: Heatmap of the associations between principal components and...
prince.var.plot: ScreePlot of the data variation covered by the principal...
quickadjust.ref: Batch adjustment by median-scaling to a reference batch
quickadjust.zero: Batch adjustment by median-centering
swamp-package: Visualization, analysis and adjustment of high-dimensional...