buildClusterTreeFromPB: Hierarchical clustering on cell types from pseudobulk

In GabrielHoffman/dreamlet: Scalable differential expression analysis of single cell transcriptomics datasets with complex study designs

Hierarchical clustering on cell types from pseudobulk

Description

Perform hierarchical clustering on cell types from pseudobulk by aggregating read counts from each cell type.

Usage

buildClusterTreeFromPB(
  pb,
  method = c("complete", "ward.D", "single", "average", "mcquitty", "median", "centroid",
    "ward.D2"),
  dist.method = c("euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski"),
  assays = assayNames(pb)
)

Arguments

pb	SingleCellObject storing pseudobulk for each cell type in in assay() field
method	clustering method for hclust()
dist.method	distance metric
assays	which assays to include

Value

hierarchical clustering object of class hclust

Examples

library(muscat)
library(SingleCellExperiment)

data(example_sce)

# create pseudobulk for each sample and cell cluster
pb <- aggregateToPseudoBulk(example_sce,
  assay = "counts",
  cluster_id = "cluster_id",
  sample_id = "sample_id",
  verbose = FALSE
)

# Hierarchical clustering of cell types
hcl <- buildClusterTreeFromPB(pb)

plot(hcl)

GabrielHoffman/dreamlet documentation built on May 20, 2024, 2:05 p.m.