BuildClusterTree: Phylogenetic Analysis of Identity Classes

In atakanekiz/Seurat3.0: Tools for Single Cell Genomics

Description

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.

Usage

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

Arguments

object	Seurat object
features	Genes to use for the analysis. Default is the set of variable genes (VariableFeatures(object = object))
dims	If set, tree is calculated in PCA space; overrides features
graph	If graph is passed, build tree based on graph connectivity between clusters; overrides dims and features
reorder	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.
reorder.numeric	Re-order identity classes according to position on the tree, assigning a numeric value ('1' is the leftmost node)
verbose	Show progress updates

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.

Value

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

Examples

pbmc_small
pbmc_small <- BuildClusterTree(object = pbmc_small)
Tool(object = pbmc_small, slot = 'BuildClusterTree')

atakanekiz/Seurat3.0 documentation built on May 26, 2019, 2:33 a.m.