Yunfei Hu

CNVeil infers haplotype-specific copy number and subclonal architecture from single-cell cancer genomes, outperforming existing methods in accuracy and resolution.

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 study uses spatial transcriptomics to map the organization of the mouse spinal cord and identifies changes in neuronal subtypes and communication patterns in neuropathic pain.

SCGclust integrates SNVs and CNAs using a graph autoencoder for cell clustering, achieving higher accuracy than existing methods on simulated and real cancer data.

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