Jingsi Ming

Funmap improves fine-mapping by integrating high-dimensional functional annotations, achieving better power and controlled false positive rates compared to existing methods.

This paper uses a mouse lemur transcriptomic atlas to identify new genes, splice junctions, and disease-related features, highlighting their relevance to primate biology and human disease.

Portal integrates large-scale single-cell datasets efficiently and accurately by preserving biological variation across samples, platforms, and species.

FIRM integrates heterogeneous scRNA-seq datasets across tissues and platforms, accurately preserving cell type identities and structure.

The paper creates a comprehensive cell atlas of mouse lemurs using single-cell RNA sequencing, identifying over 750 cell types and comparing their molecular profiles to humans, mice, and macaques.

The paper introduces SpliZ, a statistical method to detect cell-type-specific splicing regulation in single-cell RNA-seq data, finding 9.1% of genes show such regulation in human tissues.

This paper introduces a latent probit model to analyze relationships among complex traits using GWAS summary statistics and functional annotations, showing improved performance over existing methods.