clusterCells: clusterCells

In drieslab/Giotto_site_suite: Spatial Single-Cell Transcriptomics Toolbox

clusterCells

Description

cluster cells using a variety of different methods

Usage

clusterCells(
  gobject,
  cluster_method = c("leiden", "louvain_community", "louvain_multinet", "randomwalk",
    "sNNclust", "kmeans", "hierarchical"),
  name = "cluster_name",
  nn_network_to_use = "sNN",
  network_name = "sNN.pca",
  pyth_leid_resolution = 1,
  pyth_leid_weight_col = "weight",
  pyth_leid_part_type = c("RBConfigurationVertexPartition", "ModularityVertexPartition"),
  pyth_leid_init_memb = NULL,
  pyth_leid_iterations = 1000,
  pyth_louv_resolution = 1,
  pyth_louv_weight_col = NULL,
  python_louv_random = F,
  python_path = NULL,
  louvain_gamma = 1,
  louvain_omega = 1,
  walk_steps = 4,
  walk_clusters = 10,
  walk_weights = NA,
  sNNclust_k = 20,
  sNNclust_eps = 4,
  sNNclust_minPts = 16,
  borderPoints = TRUE,
  expression_values = c("normalized", "scaled", "custom"),
  genes_to_use = NULL,
  dim_reduction_to_use = c("cells", "pca", "umap", "tsne"),
  dim_reduction_name = "pca",
  dimensions_to_use = 1:10,
  distance_method = c("original", "pearson", "spearman", "euclidean", "maximum",
    "manhattan", "canberra", "binary", "minkowski"),
  km_centers = 10,
  km_iter_max = 100,
  km_nstart = 1000,
  km_algorithm = "Hartigan-Wong",
  hc_agglomeration_method = c("ward.D2", "ward.D", "single", "complete", "average",
    "mcquitty", "median", "centroid"),
  hc_k = 10,
  hc_h = NULL,
  return_gobject = TRUE,
  set_seed = TRUE,
  seed_number = 1234
)

Arguments

gobject	giotto object
cluster_method	community cluster method to use
name	name for new clustering result
nn_network_to_use	type of NN network to use (kNN vs sNN)
network_name	name of NN network to use
pyth_leid_resolution	resolution for leiden
pyth_leid_weight_col	column to use for weights
pyth_leid_part_type	partition type to use
pyth_leid_init_memb	initial membership
pyth_leid_iterations	number of iterations
pyth_louv_resolution	resolution for louvain
pyth_louv_weight_col	python louvain param: weight column
python_louv_random	python louvain param: random
python_path	specify specific path to python if required
louvain_gamma	louvain param: gamma or resolution
louvain_omega	louvain param: omega
walk_steps	randomwalk: number of steps
walk_clusters	randomwalk: number of clusters
walk_weights	randomwalk: weight column
sNNclust_k	SNNclust: k neighbors to use
sNNclust_eps	SNNclust: epsilon
sNNclust_minPts	SNNclust: min points
borderPoints	SNNclust: border points
expression_values	expression values to use
genes_to_use	= NULL,
dim_reduction_to_use	dimension reduction to use
dim_reduction_name	name of reduction 'pca',
dimensions_to_use	dimensions to use
distance_method	distance method
km_centers	kmeans centers
km_iter_max	kmeans iterations
km_nstart	kmeans random starting points
km_algorithm	kmeans algorithm
hc_agglomeration_method	hierarchical clustering method
hc_k	hierachical number of clusters
hc_h	hierarchical tree cutoff
return_gobject	boolean: return giotto object (default = TRUE)
set_seed	set seed
seed_number	number for seed

Details

Wrapper for the different clustering methods.

Value

giotto object with new clusters appended to cell metadata

See Also

doLeidenCluster, doLouvainCluster_community, doLouvainCluster_multinet, doLouvainCluster, doRandomWalkCluster, doSNNCluster, doKmeans, doHclust

drieslab/Giotto_site_suite documentation built on April 26, 2023, 11:51 p.m.