doLouvainCluster_community | R Documentation |
cluster cells using a NN-network and the Louvain algorithm from the community module in Python
doLouvainCluster_community(
gobject,
name = "louvain_clus",
nn_network_to_use = "sNN",
network_name = "sNN.pca",
python_path = NULL,
resolution = 1,
weight_col = NULL,
louv_random = F,
return_gobject = TRUE,
set_seed = F,
seed_number = 1234
)
gobject |
giotto object |
name |
name for cluster |
nn_network_to_use |
type of NN network to use (kNN vs sNN) |
network_name |
name of NN network to use |
python_path |
specify specific path to python if required |
resolution |
resolution |
weight_col |
weight column to use for edges |
louv_random |
Will randomize the node evaluation order and the community evaluation order to get different partitions at each call |
return_gobject |
boolean: return giotto object (default = TRUE) |
set_seed |
set seed |
seed_number |
number for seed |
This function is a wrapper for the Louvain algorithm implemented in Python, which can detect communities in graphs of nodes (cells). See the https://python-louvain.readthedocs.io/en/latest/index.htmlreadthedocs page for more information.
Set weight_col = NULL to give equal weight (=1) to each edge.
giotto object with new clusters appended to cell metadata
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