doLeidenCluster
¶
doLeidenCluster
Description¶
cluster cells using a NN-network and the Leiden community detection algorithm
Usage¶
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¶
Argument |
Description |
---|---|
|
giotto object |
|
name for cluster |
|
type of NN network to use (kNN vs sNN) |
|
name of NN network to use |
|
specify specific path to python if required |
|
resolution |
|
weight column to use for edges |
|
The type of partition to use for optimisation. |
|
initial membership of cells for the partition |
|
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. |
|
boolean: return giotto object (default = TRUE) |
|
set seed |
|
number for seed |
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 https://github.com/vtraag/leidenalg leidenalg github page or the https://leidenalg.readthedocs.io/en/stable/index.html readthedocs page for more information.
Partition types available and information:
list(“RBConfigurationVertexPartition: “) list(“Implements Reichardt and Bornholdt’s Potts modeln”, ” with a configuration null model. This quality function is well-defined only for positive edge weights.n”, ” This quality function uses a linear resolution parameter.”)
list(“ModularityVertexPartition: “) list(“Implements modularity.n”, ” This quality function is well-defined only for positive edge weights. It does “, list(“not”), ” use the resolution parameter”)
Set weight_col = NULL to give equal weight (=1) to each edge.
Value¶
giotto object with new clusters appended to cell metadata