cluster_regions: Cluster regions by K-means

In Shians/NanoMethViz: Visualise methylation data from Oxford Nanopore sequencing

Cluster regions by K-means

Description

Cluster regions by k-means based on their methylation profiles. In order to cluster using k-means the methylation profile of each region is interpolated and sampled at fixed points. The first 10 principal components are used for the k-means clustering. The clustering is best behaved in regions of similar width and CpG density.

Usage

cluster_regions(x, regions, centers = 2, grid_method = c("density", "uniform"))

Arguments

x	the NanoMethResult object.
regions	a table of regions containing at least columns chr, strand, start and end.
centers	number of centers for k-means, identical to the number of output clusters.
grid_method	the method for generating the sampling grid. The default option "density" attempts to create a grid with similar density as the data, "uniform" creates a grid of uniform density.

Value

the table of regions given by the 'regions' argument with the column 'cluster' added.

Examples

nmr <- load_example_nanomethresult()
gene_anno <- exons_to_genes(NanoMethViz::exons(nmr))
# uniform grid due to low number of input features
gene_anno_clustered <- cluster_regions(nmr, gene_anno, centers = 2, grid_method = "uniform")
plot_agg_regions(nmr, gene_anno_clustered, group_col = "cluster")

Shians/NanoMethViz documentation built on Oct. 11, 2024, 7:17 a.m.