| doLouvainSubCluster | R Documentation | 
subcluster cells using a NN-network and the Louvain algorithm
doLouvainSubCluster(
  gobject,
  name = "sub_louvain_clus",
  version = c("community", "multinet"),
  cluster_column = NULL,
  selected_clusters = NULL,
  hvg_param = list(reverse_log_scale = T, difference_in_cov = 1, expression_values =
    "normalized"),
  hvg_min_perc_cells = 5,
  hvg_mean_expr_det = 1,
  use_all_genes_as_hvg = FALSE,
  min_nr_of_hvg = 5,
  pca_param = list(expression_values = "normalized", scale_unit = T),
  nn_param = list(dimensions_to_use = 1:20),
  k_neighbors = 10,
  resolution = 0.5,
  gamma = 1,
  omega = 1,
  python_path = NULL,
  nn_network_to_use = "sNN",
  network_name = "sNN.pca",
  return_gobject = TRUE,
  verbose = T
)
| gobject | giotto object | 
| name | name for new clustering result | 
| version | version of Louvain algorithm to use | 
| cluster_column | cluster column to subcluster | 
| selected_clusters | only do subclustering on these clusters | 
| hvg_param | parameters for calculateHVG | 
| hvg_min_perc_cells | threshold for detection in min percentage of cells | 
| hvg_mean_expr_det | threshold for mean expression level in cells with detection | 
| use_all_genes_as_hvg | forces all genes to be HVG and to be used as input for PCA | 
| min_nr_of_hvg | minimum number of HVG, or all genes will be used as input for PCA | 
| pca_param | parameters for runPCA | 
| nn_param | parameters for parameters for createNearestNetwork | 
| k_neighbors | number of k for createNearestNetwork | 
| resolution | resolution for community algorithm | 
| gamma | gamma | 
| omega | omega | 
| python_path | specify specific path to python if required | 
| nn_network_to_use | type of NN network to use (kNN vs sNN) | 
| network_name | name of NN network to use | 
| return_gobject | boolean: return giotto object (default = TRUE) | 
| verbose | verbose | 
This function performs subclustering using the Louvain algorithm on selected clusters. The systematic steps are:
1. subset Giotto object
2. identify highly variable genes
3. run PCA
4. create nearest neighbouring network
5. do Louvain clustering
giotto object with new subclusters appended to cell metadata
doLouvainCluster_multinet and doLouvainCluster_community
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