runCluster: SNN Graph Based Community Detection

In rliger: Linked Inference of Genomic Experimental Relationships

SNN Graph Based Community Detection

Description

After aligning cell factor loadings, users can additionally run the Leiden or Louvain algorithm for community detection, which is widely used in single-cell analysis and excels at merging small clusters into broad cell classes.

While using aligned factor loadings (result from alignFactors) is recommended, this function looks for unaligned factor loadings (raw result from runIntegration) when the former is not available.

Usage

runCluster(
  object,
  resolution = 1,
  nNeighbors = 20,
  prune = 1/15,
  eps = 0.1,
  nRandomStarts = 10,
  nIterations = 5,
  method = c("leiden", "louvain"),
  useRaw = NULL,
  useDims = NULL,
  groupSingletons = TRUE,
  saveSNN = FALSE,
  clusterName = paste0(method, "_cluster"),
  seed = 1,
  verbose = getOption("ligerVerbose", TRUE)
)

Arguments

object	A liger object. Should have valid factorization result available.
resolution	Numeric, value of the resolution parameter, a larger value results in a larger number of communities with smaller sizes. Default 1.0.
nNeighbors	Integer, the maximum number of nearest neighbors to compute. Default 20.
prune	Numeric. Sets the cutoff for acceptable Jaccard index when computing the neighborhood overlap for the SNN construction. Any edges with values less than or equal to this will be set to 0 and removed from the SNN graph. Essentially sets the stringency of pruning. 0 for no pruning, while 1 prunes everything. Default 1/15.
eps	Numeric, the error bound of the nearest neighbor search. Default 0.1.
nRandomStarts	Integer number of random starts. Will pick the membership with highest quality to return. Default 10.
nIterations	Integer, maximal number of iterations per random start. Default 5.
method	Community detection algorithm to use. Choose from "leiden" or "louvain". Default "leiden".
useRaw	Whether to use un-aligned cell factor loadings (H matrices). Default NULL search for quantile-normalized loadings first and un-aligned loadings then.
useDims	Indices of factors to use for clustering. Default NULL uses all available factors.
groupSingletons	Whether to group single cells that make up their own cluster in with the cluster they are most connected to. Default TRUE, if FALSE, assign all singletons to a "singleton" group.
saveSNN	Logical, whether to store the SNN graph, as a dgCMatrix object, in the object. Default FALSE.
clusterName	Name of the variable that will store the clustering result in cellMeta slot of object. Default "leiden_cluster" and "louvain_cluster".
seed	Seed of the random number generator. Default 1.
verbose	Logical. Whether to show information of the progress. Default getOption("ligerVerbose") or TRUE if users have not set.

Value

object with cluster assignment updated in clusterName variable in cellMeta slot. Can be fetched with object[[clusterName]]. If saveSNN = TRUE, the SNN graph will be stored at object@uns$snn.

Examples

pbmcPlot <- runCluster(pbmcPlot, nRandomStarts = 1)
head(pbmcPlot$leiden_cluster)

pbmcPlot <- runCluster(pbmcPlot, method = "louvain")
head(pbmcPlot$louvain_cluster)

rliger documentation built on Aug. 27, 2025, 1:08 a.m.