Leiden Algorithm

leiden version 0.1.0

Clustering with the Leiden Algorithm in R

This package allows calling the Leiden algorthm for clustering on an igraph object from R. See the Python and Java implementations for more details:

https://github.com/CWTSLeiden/networkanalysis

https://github.com/vtraag/leidenalg

Install

This package requires the 'leidenalg' and 'igraph' modules for python (2) to be installed on your system. For example:

pip install leidenalg igraph

If you do not have root access, you can use pip install --user or pip install --prefix to install these in your user directory (which you have write permissions for) and ensure that this directory is in your PATH so that Python can find it.

The 'devtools' package will be used to install 'leiden' and the dependancies (igraph and reticulate):

if (!requireNamespace("devtools"))
    install.packages("devtools")
devtools::install_github("TomKellyGenetics/leiden")

Usage

This package provides a function to perform clustering with the Leiden algorithm:

membership <- leiden(adjacency_matrix)

For an igraph object 'graph' in R:

adjacency_matrix <- igraph::as_adjacency_matrix(graph)
membership <- leiden(adjacency_matrix)

To use Leiden with the Seurat pipeline for a Seurat Object object that has an SNN computed (for example with Seurat::FindClusters with save.SNN = TRUE). This will compute the Leiden clusters and add them to the Seurat Object Class.

adjacency_matrix <- as.matrix(object@snn)
membership <- leiden(adjacency_matrix)
object@ident <- as.factor(membership)
names(test@ident) <- rownames(test@meta.data)
object@meta.data$ident <- as.factor(membership)

These clusters can then be plotted with:

library("RColorBrewer")
colourPal <- function(groups) colorRampPalette(brewer.pal(min(length(names(table(groups))), 11), "Set3"))(length(names(table(groups))))

object <- RunPCA(object = object, do.print = TRUE, pcs.print = 1:5, genes.print = 5)
PCAPlot(object = object, colors.use = colourPal(object@ident), group.by = "ident")

object <- RunTSNE(object = object, dims.use = 1:20, do.fast = TRUE, dim.embed = 2)
TSNEPlot(object = object, colors.use = colourPal(object@ident), group.by = "ident")

object <- RunUMAP(object = object, dims.use = 1:20, metric = "correlation", max.dim = 2)
DimPlot(object, reduction.use = "umap", colors.use = colourPal(object@ident), group.by = "ident")

Citation

Please cite this implementation R in if you use it:

To cite the leiden package in publications use:

  S. Thomas Kelly (2018). leiden: R implementation of the Leiden algorithm. R
  package version 0.1.0 https://github.com/TomKellyGenetics/leiden

A BibTeX entry for LaTeX users is

  @Manual{,
    title = {leiden: R implementation of the Leiden algorithm},
    author = {S. Thomas Kelly},
    year = {2018},
    note = {R package version 0.1.0},
    url = {https://github.com/TomKellyGenetics/leiden},
  }

Please also cite the original publication of this algorithm.