SortIdents: Sorts cell metadata variable by similarity using hierarchical...

In timoast/signac: Analysis of Single-Cell Chromatin Data

Sorts cell metadata variable by similarity using hierarchical clustering

Description

Compute distance matrix from a feature/variable matrix and perform hierarchical clustering to order variables (for example, cell types) according to their similarity.

Usage

SortIdents(
  object,
  layer = "data",
  assay = NULL,
  label = NULL,
  dendrogram = FALSE,
  method = "euclidean",
  verbose = TRUE
)

Arguments

object	A Seurat object containing single-cell data.
layer	The layer of the data to use (default is "data").
assay	Name of assay to use. If NULL, use the default assay
label	Metadata attribute to sort. If NULL, uses the active identities.
dendrogram	Logical, whether to plot the dendrogram (default is FALSE).
method	The distance method to use for hierarchical clustering (default is 'euclidean', other options from dist are 'maximum', 'manhattan', 'canberra', 'binary' and 'minkowski').
verbose	Display messages

Value

The Seurat object with metadata variable reordered by similarity. If the metadata variable was a character vector, it will be converted to a factor and the factor levels set according to the similarity ordering. If active identities were used (label=NULL), the levels will be updated according to similarity ordering.

Examples

atac_small$test <- sample(1:10, ncol(atac_small), replace = TRUE)
atac_small <- SortIdents(object = atac_small, label = 'test')
print(levels(atac_small$test))

timoast/signac documentation built on Aug. 23, 2024, 1:48 a.m.