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
)giotto object
name for new clustering result
cluster column to subcluster
only do subclustering on these clusters
parameters for calculateHVG
threshold for detection in min percentage of cells
threshold for mean expression level in cells with detection
forces all genes to be HVG and to be used as input for PCA
minimum number of HVG, or all genes will be used as input for PCA
parameters for runPCA
parameters for parameters for createNearestNetwork
number of k for createNearestNetwork
resolution of Leiden clustering
number of interations to run the Leiden algorithm.
specify specific path to python if required
type of NN network to use (kNN vs sNN)
name of NN network to use
boolean: return giotto object (default = TRUE)
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