ExposureHierarchicalClustering: Hierarchical Clustering of exposure data

In rvalieris/signeR: Empirical Bayesian approach to mutational signature discovery

Hierarchical Clustering of exposure data

Description

HClustExp: Performs hierarchical clustering of samples, based on exposures.

Usage

## S4 method for signature 'SignExp,numeric'
HClustExp(signexp_obj, Med_exp=NA,
        max_instances=200, method.dist="euclidean", method.hclust="average", 
        use.cor=FALSE, relative=FALSE, plot_to_file=FALSE, 
        file="HClustExp_dendrogram.pdf", colored=TRUE)

Arguments

signexp_obj	a SignExp object returned by signeR function.
Med_exp	optional matrix with (median) exposures.
max_instances	Maximum number of the exposure matrix instances to be analyzed. If the number of available E instances is bigger than this parameter, a subset of those will be randomly selected for analysis.
method.dist	used distance metric
method.hclust	clustering method.
use.cor	used in pv.distance
relative	Whether to normalize exposures of each sample so that they sum up to one. Default is FALSE, thus clustering samples by the absolute contributions of signatures to mutation counts. Otherwise, clustering will be based on relative contributions.
plot_to_file	Whether to save a heatmap of results to the file parameter. Default is FALSE.
file	Output file to export a heatmap with the levels of pertinence of samples to found groups.
colored	Whether plots will be in color or B&W. Default is TRUE.

Value

A pvclust object, as described in package pvclust.

Examples

# assuming signatures is the return value of signeR()


HClust <- HClustExp(signatures$SignExposures)

# see also
vignette(package="signeR")

rvalieris/signeR documentation built on April 20, 2024, 2:08 p.m.