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 |
giotto object with new clusters appended to cell metadata
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.