scran::ClusterSnnGraphLeiden Class Reference

Leiden clustering on a shared nearest-neighbor graph. More...

#include <ClusterSnnGraph.hpp>

Classes
struct	Defaults
	Default parameter settings. More...
struct	Results
	Result of the igraph leiden community detection algorithm. More...

Public Member Functions
ClusterSnnGraphLeiden &	set_seed (int s=Defaults::seed)
ClusterSnnGraphLeiden &	set_resolution (double r=Defaults::resolution)
ClusterSnnGraphLeiden &	set_beta (double b=Defaults::beta)
ClusterSnnGraphLeiden &	set_iterations (int i=Defaults::iterations)
ClusterSnnGraphLeiden &	set_modularity (bool m=Defaults::modularity)
Results	run (const BuildSnnGraph::Results &store) const
Results	run (const igraph::Graph &graph, const igraph_real_t *weights) const

Detailed Description

Leiden clustering on a shared nearest-neighbor graph.

This applies Leiden clustering on a shared nearest neighbor graph. See here for more details on the Leiden algorithm.

Member Function Documentation

set_seed()

ClusterSnnGraphLeiden & scran::ClusterSnnGraphLeiden::set_seed ( int  s = Defaults::seed )

inline

Parameters

s	Seed for the default igraph random number generator.

Returns

A reference to this ClusterSnnGraphLeiden object.

set_resolution()

ClusterSnnGraphLeiden & scran::ClusterSnnGraphLeiden::set_resolution ( double  r = Defaults::resolution )

inline

Parameters

r	Resolution of the clustering. Larger values result in more fine-grained communities.

Returns

A reference to this ClusterSnnGraphLeiden object.

set_beta()

ClusterSnnGraphLeiden & scran::ClusterSnnGraphLeiden::set_beta ( double  b = Defaults::beta )

inline

Parameters

b	Level of randomness used during refinement.

Returns

A reference to this ClusterSnnGraphLeiden object.

set_iterations()

ClusterSnnGraphLeiden & scran::ClusterSnnGraphLeiden::set_iterations ( int  i = Defaults::iterations )

inline

Parameters

i	Number of iterations of the Leiden algorithm. More iterations can improve separation at the cost of computational time.

Returns

A reference to this ClusterSnnGraphLeiden object.

set_modularity()

ClusterSnnGraphLeiden & scran::ClusterSnnGraphLeiden::set_modularity ( bool  m = Defaults::modularity )

inline

Parameters

m	Whether to optimize the modularity instead of the Constant Potts Model.

The modularity is closely related to the Constant Potts Model, but the magnitude of the resolution is different.

Returns

A reference to this ClusterSnnGraphLeiden object.

run() [1/2]

Results scran::ClusterSnnGraphLeiden::run ( const BuildSnnGraph::Results &  store ) const

inline

Run the Leiden community detection algorithm on a shared nearest-neighbor graph constructed from knncolle::Base object.

Parameters

store

SNN graph built by BuildSnnGraph::run().

Returns

A Results object containing the clustering results for all cells.

run() [2/2]

Results scran::ClusterSnnGraphLeiden::run ( const igraph::Graph &  graph, const igraph_real_t *  weights  ) const

inline

Run the Leiden community detection algorithm on a pre-constructed shared nearest-neighbor graph as a Graph object.

Parameters

graph	An existing igraph::Graph object, typically built by BuildSnnGraph::Results::to_igraph().
weights	Pointer to an array of weights of length equal to the number of edges in graph.

Returns

A Results object containing the clustering results for all cells.

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The documentation for this class was generated from the following file:

• scran/clustering/ClusterSnnGraph.hpp