Yi Yang

SpatialAnno improves spatial transcriptomics annotation by integrating non-marker genes and spatial information without a reference dataset.

PRECAST integrates spatial transcriptomics data from multiple sources, improving cell and domain detection through probabilistic embedding, clustering, and alignment.

SpatialDEG identifies differentially expressed genes in spatially resolved transcriptomic data by integrating spatial information, showing controlled type-I error rates in simulations and applied to human and mouse datasets.

This paper introduces DR-SC, a method that jointly performs dimension reduction and spatial clustering for single-cell and spatial transcriptomics data, improving accuracy and biological relevance.

SC-MEB is a spatial clustering method for transcriptomics data that outperforms existing methods in clustering performance and scalability.

This paper introduces TisCoMM, a tissue-specific collaborative mixed model for TWAS that improves gene-trait association detection and identifies tissue-specific gene roles in complex traits.

This paper introduces MR-LDP, a Mendelian randomization method that accounts for linkage disequilibrium and horizontal pleiotropy, showing improved causal inference in GWAS summary statistics.

CoMM-S2 uses GWAS summary statistics to infer mechanistic roles of genetic variants in gene expression, achieving performance comparable to methods using individual-level data.