runHOPACH: runHOPACH

In adam2o1o/treekoR: Cytometry Cluster Hierarchy and Cellular-to-phenotype Associations

runHOPACH

Description

runHOPACH

Usage

runHOPACH(data, K = 10, kmax = 5, dissimilarity_metric = "cor")

Arguments

data	dataframe containing the median expression of the clusters/cell types
K	positive integer specifying the maximum number of levels in the tree. Must be 15 or less, due to computational limitations (overflow)
kmax	integer between 1 and 9 specifying the maximum number of children at each node in the tree
dissimilarity_metric	metric used to calculate dissimilarities between clusters/cell types

Value

a list containing the groups each cluster belongs to at each level of the hopach tree

Examples

library(SingleCellExperiment)
library(data.table)
data(COVIDSampleData)

sce <- DeBiasi_COVID_CD8_samp
exprs <- t(assay(sce, "exprs"))
clusters <- colData(sce)$cluster_id
classes <- colData(sce)$condition
samples <- colData(sce)$sample_id

clust_med_dt <- as.data.table(exprs)
clust_med_dt[, cluster_id := clusters]
res <- clust_med_dt[, lapply(.SD, median, na.rm=TRUE), by=cluster_id]
res2 <- res[,.SD, .SDcols = !c('cluster_id')]

hopach_res <- runHOPACH(as.data.frame(scale(res2)))

adam2o1o/treekoR documentation built on June 2, 2023, 11:42 p.m.