library(bbknnR)
library(Seurat)
#> Loading required package: SeuratObject
#> Loading required package: sp
#>
#> Attaching package: 'SeuratObject'
#> The following object is masked from 'package:base':
#>
#> intersect
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(patchwork)
data("panc8_small")
Note that RunBBKNN()
also compute t-SNE and UMAP by
default.
panc8_small <- FindClusters(panc8_small, graph.name = "bbknn")
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#>
#> Number of nodes: 500
#> Number of edges: 6417
#>
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.7193
#> Number of communities: 5
#> Elapsed time: 0 seconds