Yuge Wang

This study evaluates foundation models for single-cell data analysis across eight tasks, finding that task-specific methods may outperform them in some cases and proposing guidelines for improving FM performance.

This study benchmarks 27 computational tools for transferring cell type labels from scRNA-seq to scATAC-seq data across human and mouse tissues, finding Bridge and GLUE as top performers.

ResPAN corrects batch effects in scRNA-seq data using a deep learning framework, outperforming existing methods in batch correction and biological information preservation.

The paper introduces scAAnet, a deep autoencoder for non-linear archetypal analysis of scRNA-seq data, to identify gene expression programs and their cell-type-specific activity.