Cluster using Seurat
Usage
cluster_seurat(
sce,
assay_name,
do.scale = FALSE,
do.center = FALSE,
algorithm = 1,
resolution = 0.8,
n.neighbors = 10,
npcs.pca = 50,
features.pca = "all",
nvar.features = NULL,
dims = 1:npcs.pca,
k.param = 20,
suffix = "",
PCA_name = paste0("PCA", suffix),
UMAP_name = paste0("UMAP", suffix),
cluster_name = paste0("clusters", suffix),
umap.metric = "correlation",
annoy.metric = "cosine",
verbose = TRUE
)Arguments
- sce
SingleCellExperiment object
- assay_name
Assay name. Can provide two assay names to perform joint clustering across both
- do.scale
scale
- do.center
center
- algorithm
clustering algorithm
- resolution
clustering resolution
- n.neighbors
neighbors for umap
- npcs.pca
Total Number of PCs to compute and store (50 by default)
- features.pca
One of 'all', 'variable', or a vector of features to include in dimensionality reduction. Defaults to 'all'.
- nvar.features
Number of variable features if
features.pca='variable'- dims
Number of reduced dimensions to use for FindNeighbors and UMAP
- k.param
Defines k for the k-nearest neighbor algorithm
- suffix
Suffix name to add to the PCA, UMAP, and clusters
- PCA_name
Name to store PCA dimred
- UMAP_name
Name to store UMAP dimred
- cluster_name
Name to store seurat clusters
- umap.metric
Metric for Seurat::RunUMAP
- annoy.metric
Metric for Seurat::FindNeighbors
- verbose
Message verbosity