perform.graph.cluster: Performs graph-based clustering

In connorhknight/IBRAP: Integrated Benchmarking scRNA-seq Analytical Pipeline (IBRAP)

Performs graph-based clustering

Description

Performs graph-based clustering on previously generated neighbouhood graphs.

Usage

perform.graph.cluster(
  object,
  assay,
  neighbours,
  algorithm = 1,
  cluster.df.name.suffix = "",
  res = c(0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.1, 1.2, 1.3, 1.4, 1.5),
  verbose = FALSE,
  seed = 1234,
  ...
)

Arguments

object	IBRAP S4 class object
assay	Character. String containing indicating which assay to use
neighbours	Character. String indicating which neighbourhood graphs should be used.
algorithm	Numerical. Algorithm for modularity optimization (1 = original Louvain algorithm; 2 = Louvain algorithm with multilevel refinement; 3 = SLM algorithm; 4 = Leiden algorithm). Leiden requires the leidenalg python. Default = 1 Default = NULL
res	Numerical vector. Which resolution to run the clusterign algorithm at, a smaller and larger value identified less and more clusters, respectively. Default = c(0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1,1.1,1.2,1.3,1.4,1.5)
verbose	Logical. Should system information be printed. Default = FALSE
seed	Numeric. What should the seed be set as. Default = 1234
...	arguments to be passed to Seurat::FindClusters
cluster.df.name	Character. What to call the df contained in clusters. Default = 'seurat

Value

Cluster assignments using the list of resolutions provided contained within cluster_assignments under cluster.df.name

Examples

object <- perform.nn.v1(object = object, assay = c('SCT', 'SCRAN', 'SCANPY'), 
                        reduction = c('pca_harmony','scanorama'), 
                        dims = list(0,0), generate.diffmap = T)

object <- perform.nn.v1(object = object, assay = c('SCT', 'SCRAN', 'SCANPY'), 
                        reduction = c('pca_bbknn_bbknn:diffmap','pca_harmony_nn.v1:diffmap', 'scanorama_nn.v1:diffmap'), 
                        dims = list(0,0,0))

object <- perform.nn.v2(object = object, assay = c('SCT', 'SCRAN', 'SCANPY'), 
                       reduction = c('pca_harmony','scanorama','pca_bbknn_bbknn:diffmap',
                                     'pca_harmony_nn.v1:diffmap', 'scanorama_nn.v1:diffmap'), 
                       dims = list(0,0,0,0,0))
                       
object <- perform.graph.cluster(object = object, assay = c('SCT', 'SCRAN', 'SCANPY'), 
                                neighbours = c("pca_bbknn_bbknn",
                                               "pca_harmony_nn.v1",
                                               "scanorama_nn.v1",
                                               "pca_bbknn_bbknn:diffmap_nn.v1",
                                               "pca_harmony_nn.v1:diffmap_nn.v1",
                                               "scanorama_nn.v1:diffmap_nn.v1",
                                               "pca_harmony_nn.v2",
                                               "scanorama_nn.v2",
                                               "pca_bbknn_bbknn:diffmap_nn.v2",
                                               "pca_harmony_nn.v1:diffmap_nn.v2",
                                               "scanorama_nn.v1:diffmap_nn.v2" ), 
                                algorithm = 1)

connorhknight/IBRAP documentation built on March 9, 2023, 7:01 p.m.