clusteringKmeansNestedHclust: 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 the contents of each cluster are sorted using hclust

Description

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

Usage

clusteringKmeansNestedHclust(
  mat,
  nclust,
  within_order_strategy = valid_sort_strategies[2],
  centroids = NULL,
  manual_mapping = NULL,
  iter.max = 30
)

Arguments

mat	A wide format matrix
nclust	the number of clusters
within_order_strategy	one of "hclust", "sort", "right", "left", "reverse". If "hclust", hierarchical clustering will be used. If "sort", a simple decreasing sort of rosSums. If "left", will atttempt to put high signal on left ("right" is opposite). If "reverse" reverses existing order (should only be used after meaningful order imposed).
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.
manual_mapping	optional named vector manually specififying cluster assignments. names should be item ids and values should be cluster names the items are assigned to. Default of NULL allows clustering to proceed.
iter.max	Number of max iterations to allow for k-means. Default is 30.

Value

data.table with 2 columns of cluster info. id__ column corresponds with input matrix rownames and is sorted within each cluster using hierarchical clusering group__ column indicates cluster assignment

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 = clusteringKmeansNestedHclust(mat, nclust = 3)
clust_dt

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