Clustering with the Leiden Algorithm in R
This package allows calling the Leiden algorithm 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). To install the development version:
if (!requireNamespace("devtools"))
install.packages("devtools")
devtools::install_github("TomKellyGenetics/leiden")
The current release on CRAN can be installed with:
install.packages("leiden")
library("leiden")
Usage
Running the Leiden algorithm in R
First set up a compatible adjacency matrix:
adjacency_matrix <- rbind(cbind(matrix(round(rbinom(400, 1, 0.8)), 20, 20),
matrix(round(rbinom(400, 1, 0.3)), 20, 20),
matrix(round(rbinom(400, 1, 0.1)), 20, 20)),
cbind(matrix(round(rbinom(400, 1, 0.3)), 20, 20),
matrix(round(rbinom(400, 1, 0.8)), 20, 20),
matrix(round(rbinom(400, 1, 0.2)), 20, 20)),
cbind(matrix(round(rbinom(400, 1, 0.3)), 20, 20),
matrix(round(rbinom(400, 1, 0.1)), 20, 20),
matrix(round(rbinom(400, 1, 0.9)), 20, 20)))
str(adjacency_matrix)
#> num [1:60, 1:60] 1 1 1 1 1 1 0 0 1 0 ...
dim(adjacency_matrix )
#> [1] 60 60
An adjacency matrix is any binary matrix representing links between nodes (column and row names). It is a directed graph if the adjacency matrix is not symmetric.
library("igraph")
rownames(adjacency_matrix) <- 1:60
colnames(adjacency_matrix) <- 1:60
graph_object <- graph_from_adjacency_matrix(adjacency_matrix, mode = "directed")
graph_object
#> IGRAPH 3ffce81 DN-- 60 1516 --
#> + attr: name (v/c)
#> + edges from 3ffce81 (vertex names):
#> [1] 1->1 1->3 1->7 1->8 1->10 1->11 1->13 1->15 1->16 1->18 1->19 1->20 1->21 1->30 1->35 1->51 2->1 2->2 2->4
#> [20] 2->6 2->7 2->9 2->10 2->11 2->12 2->13 2->14 2->15 2->16 2->17 2->18 2->19 2->20 2->23 2->30 2->33 2->34 2->47
#> [39] 2->52 2->58 2->60 3->1 3->2 3->3 3->4 3->5 3->6 3->7 3->8 3->9 3->11 3->12 3->13 3->14 3->15 3->16 3->17
#> [58] 3->18 3->19 3->20 3->23 3->26 3->28 3->30 3->37 3->39 3->55 4->1 4->2 4->3 4->4 4->5 4->6 4->7 4->8 4->10
#> [77] 4->11 4->12 4->13 4->14 4->15 4->16 4->20 4->21 4->25 4->29 4->30 4->31 4->47 4->56 5->1 5->4 5->5 5->6 5->7
#> [96] 5->8 5->9 5->10 5->13 5->14 5->15 5->16 5->17 5->18 5->20 5->23 5->37 5->38 5->41 6->1 6->2 6->3 6->5 6->6
#> [115] 6->7 6->9 6->10 6->11 6->12 6->14 6->15 6->17 6->18 6->19 6->20 6->22 6->23 6->24 6->27 6->34 6->37 6->38 6->39
#> [134] 6->40 6->41 6->45 6->60 7->2 7->4 7->5 7->6 7->7 7->8 7->10 7->11 7->12 7->13 7->14 7->15 7->16 7->17 7->18
#> + ... omitted several edges
This represents the following graph structure.
plot(graph_object, vertex.color = "grey75")
plot of chunk unnamed-chunk-7
Then the Leiden algorithm can be run on the igraph object.
partition <- leiden(graph_object)
#> Warning in paste(el[, 1], el[, 2], sep = "|"): NAs introduced by coercion to integer range
#> Warning in paste(el[, 1], el[, 2], sep = "|"): NAs introduced by coercion to integer range
table(partition)
#> partition
#> 1 2 3
#> 20 20 20
Here we can see partitions in the plotted results. The nodes that are more interconnected have been partitioned into separate clusters.
library("RColorBrewer")
node.cols <- brewer.pal(max(c(3, partition)),"Pastel1")[partition]
plot(graph_object, vertex.color = node.cols)
plot of chunk unnamed-chunk-11
Running Leiden with arguments passed to leidenalg
Arguments can be passed to the leidenalg implementation in Python:
#run with defaults
partition <- leiden(graph_object)
#> Warning in paste(el[, 1], el[, 2], sep = "|"): NAs introduced by coercion to integer range
#> Warning in paste(el[, 1], el[, 2], sep = "|"): NAs introduced by coercion to integer range
#run with ModularityVertexPartition"
partition <- leiden(graph_object, partition_type = "ModularityVertexPartition")
#> Warning in paste(el[, 1], el[, 2], sep = "|"): NAs introduced by coercion to integer range
#> Warning in paste(el[, 1], el[, 2], sep = "|"): NAs introduced by coercion to integer range
#run with resolution parameter
partition <- leiden(graph_object, resolution_parameter = 0.95)
#> Warning in paste(el[, 1], el[, 2], sep = "|"): NAs introduced by coercion to integer range
#> Warning in paste(el[, 1], el[, 2], sep = "|"): NAs introduced by coercion to integer range
In particular, the resolution parameter can tune the number of clusters to be detected.
partition <- leiden(graph_object, resolution_parameter = 0.5)
node.cols <- brewer.pal(max(c(3, partition)),"Pastel1")[partition]
plot(graph_object, vertex.color = node.cols)
partition <- leiden(graph_object, resolution_parameter = 1.8)
node.cols <- brewer.pal(max(c(3, partition)),"Pastel1")[partition]
plot(graph_object, vertex.color = node.cols)
Weights for edges an also be passed to the leiden algorithm either as a separate vector or as a weighted graph_object. Weights will be derived from a weighted graph object.
# generate (unweighted) igraph object in R
library("igraph")
adjacency_matrix[adjacency_matrix > 1] <- 1
snn_graph <- graph_from_adjacency_matrix(adjacency_matrix)
partition <- leiden(snn_graph)
table(partition)
#> partition
#> 1 2 3
#> 20 20 20
# pass weights to python leidenalg
adjacency_matrix[adjacency_matrix >= 1 ] <- 1
snn_graph <- graph_from_adjacency_matrix(adjacency_matrix, weighted = NULL)
weights <- sample(1:10, sum(adjacency_matrix!=0), replace=TRUE)
partition <- leiden(snn_graph, weights = weights)
table(partition)
#> partition
#> 1 2 3
#> 20 20 20
# generate (weighted) igraph object in R
library("igraph")
adjacency_matrix[adjacency_matrix >= 1] <- weights
snn_graph <- graph_from_adjacency_matrix(adjacency_matrix, weighted = TRUE)
partition <- leiden(snn_graph)
table(partition)
#> partition
#> 1 2 3
#> 20 20 20
See the documentation on the leidenalg Python module for more information: https://leidenalg.readthedocs.io/en/latest/reference.html
Running on a Seurat Object
Seurat version 2
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. The R implementation of Leiden can be run directly on the snn igraph object in Seurat.
library("Seurat")
FindClusters(pbmc_small)
membership <- leiden(pbmc_small@snn)
table(membership)
pbmc_small@ident <- as.factor(membership)
names(pbmc_small@ident) <- rownames(pbmc_small@meta.data)
pbmc_small@meta.data$ident <- as.factor(membership)
Note that this code is designed for Seurat version 2 releases.
Seurat version 3 or later
Note that the object for Seurat version 3 has changed. For example an SNN can be generated:
library("Seurat")
FindClusters(pbmc_small)
membership <- leiden(pbmc_small@graphs$RNA_snn)
table(membership)
For Seurat version 3 objects, the Leiden algorithm has been implemented in the Seurat version 3 package with Seurat::FindClusters and algorithm = "leiden"). See the documentation for these functions.
FindClusters(pbmc_small, algorithm = "leiden")
table(pbmc_small@active.ident)