Guanao Yan

MorphoANOVA systematically analyzes spatial gene-expression patterns in relation to tissue morphology, identifying genes with location-specific associations and spatial expression beyond morphology.

CellScope improves cell atlas construction by integrating UMAP with hierarchical clustering, enhancing clustering performance, visualization, and interpretability across single-cell datasets.

AIGS improves scRNA-seq analysis by selecting informative genes and using a scale-invariant distance metric, achieving better clustering and visualization.

This study benchmarks 14 computational methods for identifying spatially variable genes and peaks using simulated and real-world spatial transcriptomics and ATAC-seq data, finding spatialDE2 performs best and highlighting the need for specialized algorithms for peaks.

scReadSim generates synthetic scRNA-seq and scATAC-seq reads with user-specified ground truths, enabling benchmarking of computational tools.

The paper introduces scDesign3, a statistical simulator that generates realistic single-cell and spatial omics data and infers biologically meaningful parameters for benchmarking computational tools.

This paper introduces RA3, a reference-guided method for analyzing single-cell chromatin accessibility data, showing improved performance across multiple datasets.