Wenjun Shen

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.

CSsingle improves bulk and spatial transcriptomic data decomposition by integrating with scRNA-seq, showing better accuracy and revealing MCCs as potential biomarkers in EAC.

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

MaskGraphene integrates multi-slice, multi-condition spatial transcriptomics data using a graph neural network to generate interpretable joint embeddings with preserved geometric information, improving downstream analyses.

Bfimpute is a Bayesian factorization method for imputing single-cell RNA-seq data, achieving better accuracy than existing methods on simulated and real data.