doLouvainSubCluster_multinet | R Documentation |
subcluster cells using a NN-network and the Louvain multinet detection algorithm
doLouvainSubCluster_multinet(
gobject,
name = "sub_louvain_mult_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,
gamma = 1,
omega = 1,
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 |
gamma |
gamma |
omega |
omega |
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 multinet 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 multinet clustering
giotto object with new subclusters appended to cell metadata
doLouvainCluster_multinet
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