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
)giotto object
name for new clustering result
version of Louvain algorithm to use
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 for community algorithm
gamma
omega
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 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