`clusterCells`¶

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 = T,
  seed_number = 1234
)

Arguments¶

Argument	Description
`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

Seealso¶

``doLeidenCluster` <#doleidencluster>`_ , ``doLouvainCluster_community` <#dolouvainclustercommunity>`_ , ``doLouvainCluster_multinet` <#dolouvainclustermultinet>`_ ,: ``doLouvainCluster` <#dolouvaincluster>`_ , ``doRandomWalkCluster` <#dorandomwalkcluster>`_ , ``doSNNCluster` <#dosnncluster>`_ , ``doKmeans` <#dokmeans>`_ , ``doHclust` <#dohclust>`_