Further Sub-Clustering of Cells Using NN-Network and Louvain Algorithm¶

doLouvainSubCluster()

Further sub-clustering of 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
)

Arguments¶

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

Value¶

Giotto object with new sub-clusters appended to cell metadata

Details¶

This function performs subclustering using the Leiden algorithm on selected clusters.

The systematic steps are:

Subset Giotto object
Identify highly variable genes
Run PCA
Create nearest neighbouring network
Do Louvain clustering