Variable Neighborhood Search Approach for Cell Clustering

The Variable Neighborhood Search (VNS) method is well known metaheuristic method, which starts from one point from the search space, explores its neighborhoods and repeats the whole process until better solution is found or some stopping criteria is reached. Leveraging the well-established foundation of VNS, first we present a comprehensive solution for the cell clustering problem in the form of the Integer Linear Programming (ILP) minimization problem, which is based on the p-median classification. The proposed algorithm exhibits the ability to organize cells into clusters, utilizing information from both gene expression matrices and spatial coordinates.

Parameters

Name	Type	Definition	Default
adata_path	str	Path to adata file	data.h5ad
emb	str	Choose embeddings ('CCST', 'GraphST', 'STAGATE', 'X_pca', 'STAligner')	X_pca
k	int	Number of clusters	20
max_iter	int	Maximal number of iterations	20
p	int	LocalSearch parameter	12
m	int	LocalSearch parameter	15
alpha	int	Precentage of the influence of the embedding values	1
s	int	Maximal number of neighborhoods that should be searched	10
seed	int	Seed value	1234

To run the code, you can use the following example command:

python vns.py --adata_path '/goofys/BCO/Benchmark/SS200000128TR_E2_benchmark.h5ad' --ground_truth 'celltype_pred' --emb 'GraphST' --k 33 --max_iter 10 --param 12 --m 15 --alpha 1 --s 20 --seed 4639