Haoran Qin

This study benchmarks 20 cell type annotation methods on spatial transcriptomics data to evaluate their performance in resolving cell types, subtypes, and biological organization across diverse datasets.

BioLACE improves spatial clustering in transcriptomics by integrating spatial geometry and marker gene priors within a VAE framework, achieving better cell type accuracy and interpretability.

This paper benchmarks clustering, alignment, and integration methods for spatial transcriptomics using real and simulated datasets, providing recommendations and code for reproducibility.