clusterSamples-methods: Hierarchical Clustering using methylation data

In fortunatobianconi/methylkit: DNA methylation analysis from high-throughput bisulfite sequencing results

Description

The function clusters samples using hclust function and various distance metrics derived from percent methylation per base or per region for each sample.

Usage

  clusterSamples(.Object, dist="correlation",
    method="ward", sd.filter=TRUE,sd.threshold=0.5,
    filterByQuantile=TRUE, plot=TRUE)

Arguments

.Object	a methylBase object
dist	the distance measure to be used. This must be one of "correlation", "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski". Any unambiguous abbreviation can be given. (default:"correlation")
method	the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of "ward", "single", "complete", "average", "mcquitty", "median" or "centroid". (default:"ward")
sd.filter	If TRUE, the bases/regions with low variation will be discarded prior to clustering (default:TRUE)
sd.threshold	A numeric value. If filterByQuantile is TRUE, features whose standard deviations is less than the quantile denoted by sd.threshold will be removed. If filterByQuantile is FALSE, then features whose standard deviations is less than the value of sd.threshold will be removed.(default:0.5)
filterByQuantile	A logical determining if sd.threshold is to be interpreted as a quantile of all Standard Deviation values from bases/regions (the default), or as an absolute value
plot	a logical value indicating whether to plot hierarchical clustering. (default:TRUE)

Value

a tree object of a hierarchical cluster analysis using a set of dissimilarities for the n objects being clustered.

Examples

data(methylKit)

clusterSamples(methylBase.obj, dist="correlation", method="ward", plot=TRUE)

fortunatobianconi/methylkit documentation built on May 16, 2019, 1:51 p.m.