get_highest_prune_param_embedding: Calculate the highest pruning parameter for the SNN graph...

In Core-Bioinformatics/ClustAssess: Tools for Assessing Clustering

Calculate the highest pruning parameter for the SNN graph given Embedding

Description

Given an embedding, the function calculates the highest pruning parameter for the SNN graph that preserves the connectivity of the graph.

Usage

get_highest_prune_param_embedding(embedding, n_neigh)

Arguments

embedding	A matrix associated with a PCA embedding. Embeddings from other dimensionality reduction techniques (such as LSI) can be used.
n_neigh	The number of nearest neighbours.

Value

The value of the highest pruning parameter.

Note

Given the way the SNN graph is built, the possible values for the pruning parameter are limited and can be determined by the formula i / (2 * n_neigh - i), where i is a number of nearest neighbours between 0 and n_neigh.

Examples

set.seed(2024)
# create an artificial pca embedding
pca_embedding <- matrix(
    c(runif(100 * 10), runif(100 * 10, min = 3, max = 4)),
    nrow = 200, byrow = TRUE
)
rownames(pca_embedding) <- as.character(1:200)
colnames(pca_embedding) <- paste("PC", 1:10)

get_highest_prune_param_embedding(pca_embedding, 5)

Core-Bioinformatics/ClustAssess documentation built on Nov. 4, 2024, 1:05 p.m.