Cluster Cells Using NN-Network

doLeidenCluster()

Cluster cells using a NN-network and the Leiden community detection algorithm.

doLeidenCluster(
    gobject,
    name = "leiden_clus",
    nn_network_to_use = "sNN",
    network_name = "sNN.pca",
    python_path = NULL,
    resolution = 1,
    weight_col = "weight",
    partition_type = c("RBConfigurationVertexPartition", "ModularityVertexPartition"),
    init_membership = NULL,
    n_iterations = 1000,
    return_gobject = TRUE,
    set_seed = T,
    seed_number = 1234
)

Arguments

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
partition_type	The type of partition to use for optimisation.
init_membership	initial membership of cells for the partition
n_iterations	number of interations to run the Leiden algorithm. If the number of iterations is negative, the Leiden algorithm is run until an iteration in which there was no improvement.
return_gobject	boolean: return giotto object (default = TRUE)
set_seed	set seed
seed_number	number for seed

Value

Giotto object with new clusters appended to cell metadata

Details

This function is a wrapper for the Leiden algorithm implemented in python, which can detect communities in graphs of millions of nodes (cells), as long as they can fit in memory. See the leidenalg Github page or the leidenalg readthedocs page for more information.

Partition types available and information:

• RBConfigurationVertexPartition: Implements Reichardt and Bornholdt’s Potts model with a configuration null model. This quality function is well-defined only for positive edge weights. This quality function uses a linear resolution parameter.

• ModularityVertexPartition: Implements modularity. This quality function is well-defined only for positive edge weights. It does not use the resolution parameter

Set weight_col = NULL to give equal weight (=1) to each edge.