Haotian Zhuang

Halo improves whole-cell segmentation in spatial transcriptomics by integrating nuclear morphology and RNA transcript distribution, outperforming existing methods across diverse tissues.

MUSTARD reduces dimensionality of multi-sample scRNA-seq data using trajectory guidance, revealing sample-level heterogeneity and associated gene markers.

PreTSA efficiently models temporal and spatial gene expression patterns in large single-cell and spatial transcriptomics datasets, matching state-of-the-art results with reduced computational time.

findPC automatically selects the optimal number of principal components for single-cell data using six methods, with the perpendicular line method performing best on real RNA-seq data.