Constructs a phylogenetic tree relating the 'average' cell from each identity class. Tree is estimated based on a distance matrix constructed in either gene expression space or PCA space.

BuildClusterTree(
object,
assay = NULL,
features = NULL,
dims = NULL,
graph = NULL,
slot = "data",
reorder = FALSE,
reorder.numeric = FALSE,
verbose = TRUE
)

## Arguments

object Seurat object Assay to use for the analysis. Genes to use for the analysis. Default is the set of variable genes (VariableFeatures(object = object)) If set, tree is calculated in PCA space; overrides features If graph is passed, build tree based on graph connectivity between clusters; overrides dims and features Slot(s) to use; if multiple slots are given, assumed to follow the order of 'assays' (if specified) or object's assays Re-order identity classes (factor ordering), according to position on the tree. This groups similar classes together which can be helpful, for example, when drawing violin plots. Re-order identity classes according to position on the tree, assigning a numeric value ('1' is the leftmost node) Show progress updates

## Value

A Seurat object where the cluster tree can be accessed with Tool

## Details

Note that the tree is calculated for an 'average' cell, so gene expression or PC scores are averaged across all cells in an identity class before the tree is constructed.

## Examples

data("pbmc_small")
pbmc_small
#> An object of class Seurat
#> 230 features across 80 samples within 1 assay
#> Active assay: RNA (230 features, 20 variable features)
#>  2 dimensional reductions calculated: pca, tsnepbmc_small <- BuildClusterTree(object = pbmc_small)
Tool(object = pbmc_small, slot = 'BuildClusterTree')
#>
#> Phylogenetic tree with 3 tips and 2 internal nodes.
#>
#> Tip labels:
#>   0, 1, 2
#>
#> Rooted; includes branch lengths.