Further subcluster cells using a NN-network and the Leiden algorithm
doLeidenSubCluster( gobject, name = "sub_pleiden_clus", 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, n_iterations = 500, 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 |
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 of Leiden clustering |
n_iterations | number of interations to run the Leiden algorithm. |
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 |
giotto object with new subclusters appended to cell metadata
This function performs subclustering using the Leiden 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 Leiden clustering