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bbknnR Tutorial

Compiled: 2024-02-13

Setup library and data

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")

Run BBKNN

Note that RunBBKNN() also compute t-SNE and UMAP by default.

panc8_small <- RunBBKNN(panc8_small, batch_key = "tech")
#> Running BBKNN using RcppAnnoy...
#> Running UMAP...
#> Running tSNE...
#> All done!

Find Clusters using bbknn graph

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

Visualization

p1 <- DimPlot(panc8_small, reduction = "umap", group.by = "celltype", label = TRUE,
             label.size = 3 , repel = TRUE) + NoLegend()
p2 <- DimPlot(panc8_small, reduction = "umap", group.by = "tech")
p3 <- DimPlot(panc8_small, reduction = "umap")

wrap_plots(list(p1, p2, p3), ncol = 1)

p1 <- DimPlot(panc8_small, reduction = "tsne", group.by = "celltype", label = TRUE,
             label.size = 3 , repel = TRUE) + NoLegend()
p2 <- DimPlot(panc8_small, reduction = "tsne", group.by = "tech")
p3 <- DimPlot(panc8_small, reduction = "tsne")

wrap_plots(list(p1, p2, p3), ncol = 1)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.