clusterCells

cluster cells using a variety of different methods

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 = FALSE,
  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"),
  feats_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
feats_to_use: features to use in clustering,
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

Value

giotto object with new clusters appended to cell metadata

Details

Wrapper for the different clustering methods.

Examples

g <- GiottoData::loadGiottoMini("visium")
#> 1. read Giotto object
#> 2. read Giotto feature information
#> 3. read Giotto spatial information
#> 3.1 read Giotto spatial shape information
#> 3.2 read Giotto spatial centroid information
#> 3.3 read Giotto spatial overlap information
#> 4. read Giotto image information
#> python already initialized in this session
#>  active environment : '/usr/bin/python3'
#>  python version : 3.10
#> checking default envname 'giotto_env'
#> a system default python environment was found
#> Using python path:
#>  "/usr/bin/python3"

clusterCells(g)
#> Error in py_run_file_impl(file, local, convert): ModuleNotFoundError: No module named 'igraph'
#> Run `reticulate::py_last_error()` for details.

Arguments

Value

Details

See also

Examples