perform.graph.subclustering: Seurat subclustering

In connorhknight/IBRAP_no_decontX: Integrated Benchmarking scRNA-seq Analytical Pipeline

Description

Seurat subclustering

Usage

perform.graph.subclustering(
  object,
  assay,
  clust.method,
  column,
  clusters,
  neighbours,
  algorithm = 1,
  res = 0.6,
  ...
)

Arguments

object	An IBRAP S4 class object
assay	Character. Which assay within the object to access
clust.method	Character. Which cluster_assignments dataframe to access
column	Character. Which column to access within the cluster_assignment dataframe
clusters	Which cluster(s) would you like to subcluster
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)
...	arguments to be passed to Seurat::FindClusters
cluster.df.name	Character. What to call the df contained in clusters. Default = 'seurat

Value

A new column within the defined cluster_assignment dataframe containing original and new subclusters

Examples

object <- perform.graph.subclustering(object = object, assay = 'SCT', 
                                      clust.method = 'pca', 
                                      column = 'neighbourhood_graph_res.0.7', clusters = c(1,5,9), 
                                      neighbours = 'pca_nn:', algorithm = 1)

connorhknight/IBRAP_no_decontX documentation built on Feb. 13, 2022, 2:32 p.m.