clusteringKmeans: perform kmeans clustering on matrix rows and return reordered...

In jrboyd/seqsetvis: Set Based Visualizations for Next-Gen Sequencing Data

perform kmeans clustering on matrix rows and return reordered matrix along with order matched cluster assignments. clusters are sorted using hclust on centers

Description

perform kmeans clustering on matrix rows and return reordered matrix along with order matched cluster assignments. clusters are sorted using hclust on centers

Usage

clusteringKmeans(mat, nclust, centroids = NULL, iter.max = 30)

Arguments

mat	numeric matrix to cluster.
nclust	the number of clusters.
centroids	optional matrix with same columns as mat and one centroid per row to base clusters off of. Overrides any setting to nclust. Default of NULL results in randomly initialized k-means.
iter.max	Number of max iterations to allow for k-means. Default is 30.

Value

data.table with group__ variable indicating cluster membership and id__ variable that is a factor indicating order based on within cluster similarity

Examples

data(CTCF_in_10a_profiles_dt)
dt = data.table::copy(CTCF_in_10a_profiles_dt)
mat = data.table::dcast(dt, id ~ sample + x, value.var = "y" )
rn = mat$id
mat = as.matrix(mat[,-1])
rownames(mat) = rn
clust_dt = clusteringKmeans(mat, nclust = 3)
dt = merge(dt, clust_dt[, .(id = id__, group = group__)])
dt$id = factor(dt$id, levels = clust_dt$id)
dt[order(id)]

jrboyd/seqsetvis documentation built on Dec. 10, 2024, 11:23 a.m.