SortIdents function

Sorts cell metadata variable by similarity using hierarchical clustering

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

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

Returns

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))