Chen Lin

CONCISE improves spatial ligand-receptor interaction inference by accounting for spatial autocorrelation and measurement errors, reducing false positives and enhancing detection power in spatial transcriptomics data.

The paper introduces CASE, a Bayesian framework for cell-type-specific and shared eQTL fine-mapping, showing improved performance over existing methods in simulations and real data.

This study identifies genetic loci influencing immune cell proportions and their associations with autoimmune diseases using GWAS and cWAS.

The paper presents a framework to analyze multi-omic risk factors for coronary artery disease using TWAS and PWAS, identifying 10 genes with shared associations and colocalized eQTLs and pQTLs.

This paper introduces a statistical framework, cWAS, to identify cell types with genetically regulated proportions associated with complex diseases using integrated genetic and transcriptomics data.