doLeidenSubCluster | R Documentation |
Further subcluster cells using a NN-network and the Leiden algorithm
doLeidenSubCluster(
gobject,
feat_type = NULL,
name = "sub_pleiden_clus",
cluster_column = NULL,
selected_clusters = NULL,
hvf_param = list(reverse_log_scale = TRUE, difference_in_cov = 1, expression_values =
"normalized"),
hvg_param = NULL,
hvf_min_perc_cells = 5,
hvg_min_perc_cells = NULL,
hvf_mean_expr_det = 1,
hvg_mean_expr_det = NULL,
use_all_feats_as_hvf = FALSE,
use_all_genes_as_hvg = NULL,
min_nr_of_hvf = 5,
min_nr_of_hvg = NULL,
pca_param = list(expression_values = "normalized", scale_unit = TRUE),
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 = TRUE
)
gobject |
giotto object |
feat_type |
feature type |
name |
name for new clustering result |
cluster_column |
cluster column to subcluster |
selected_clusters |
only do subclustering on these clusters |
hvf_param |
parameters for calculateHVf |
hvg_param |
deprecatd, use hvf_param |
hvf_min_perc_cells |
threshold for detection in min percentage of cells |
hvg_min_perc_cells |
deprecated, use hvf_min_perc_cells |
hvf_mean_expr_det |
threshold for mean expression level in cells with detection |
hvg_mean_expr_det |
deprecated, use hvf_mean_expr_det |
use_all_feats_as_hvf |
forces all features to be HVF and to be used as input for PCA |
use_all_genes_as_hvg |
deprecated, use use_all_feats_as_hvf |
min_nr_of_hvf |
minimum number of HVF, or all features will be used as input for PCA |
min_nr_of_hvg |
deprecated, use min_nr_of_hvf |
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 |
This function performs subclustering using the Leiden algorithm on selected clusters. The systematic steps are:
1. subset Giotto object
2. identify highly variable fetures
3. run PCA
4. create nearest neighbouring network
5. do Leiden clustering
giotto object with new subclusters appended to cell metadata
doLeidenCluster
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