cluster_sce: Cluster SingleCellExperiment with monocle3::cluster_cells

In combiz/scFlow: A Single-Cell/Nuclei Analysis Toolkit

Cluster SingleCellExperiment with monocle3::cluster_cells

Description

Cluster SingleCellExperiment with monocle3::cluster_cells

Usage

cluster_sce(
  sce,
  cluster_method = "louvain",
  reduction_method = "UMAP_Liger",
  resolution = NULL,
  k = 50,
  louvain_iter = 1,
  verbose = T,
  ...
)

Arguments

sce	a SingleCellExperiment object
cluster_method	Available methods include leiden and louvain.
reduction_method	Input slot for clustering. Available options are PCA, UMAP_Liger, tSNE_Liger.
resolution	Clustering resolution. If NULL, clustering method will be set to louvain.
k	Integer number of nearest neighbors to use when creating the k nearest neighbor graph for Louvain/Leiden clustering. k is related to the resolution of the clustering result, a bigger k will result in lower resolution and vice versa. Default is 50.
louvain_iter	Integer number of iterations used for Louvain clustering. The clustering result giving the largest modularity score will be used as the final clustering result. Default is 1. Note that if num_iter is greater than 1, the random_seed argument will be ignored for the louvain method.
verbose	A logic flag to determine whether or not we should print the run details.
...	see monocle3::cluster_cells for more clustering options.

Value

sce a SingleCellExperiment object annotated with reducedDims

See Also

Other clustering and dimensionality reduction: .get_x_most_specific_genes(), .map_celltypes(), .map_celltypes_with_ewce(), map_custom_celltypes(), reduce_dims_sce()

combiz/scFlow documentation built on Feb. 25, 2024, 10:25 a.m.