runHOPACH {treekoR} | R Documentation

## 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)))

[Package treekoR version 1.2.1 Index]