Fast Leiden Clustering Using Sparse Shared Nearest Neighbors
Usage
leiden_embedding_fast(
data,
k = 30,
prune.snn = 0,
weight = "jaccard",
resolution = 1
)
Arguments
- data
A numeric matrix of embeddings (e.g., PCA, UMAP).
- k
Integer. Number of nearest neighbors (default: 30).
- prune.snn
Numeric threshold to prune weak SNN edges (default: 0).
- weight
Character. Weighting scheme (currently unused; default: `"jaccard"`).
- resolution
Resolution parameter for Leiden clustering (default: 1).
Value
A factor vector of cluster labels.
Details
Efficient implementation of SNN graph construction and Leiden clustering.
Uses matrix algebra for fast computation of shared neighbors and Jaccard similarity.
Examples
mat <- matrix(rnorm(500), nrow = 100)
clusters <- leiden_embedding_fast(mat, k = 20)
#> Error in if (ncol(result) < 3) { result <- cbind(result, 1)}: argument is of length zero
table(clusters)
#> Error in table(clusters): object 'clusters' not found