Xiangyu Zhang

INSPIRE integrates and interprets multiple spatial transcriptomics datasets, revealing tissue structures and biological processes across diverse samples and technologies.

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.

The paper introduces UKBioBERT, a DNA language model pre-trained on UK BioBank variants, which improves gene expression prediction and enables eQTL identification when combined with sequence-to-function models.

This paper introduces TWASKnockoff, a method that improves causal gene identification in TWAS by accounting for gene correlations and genetic variant effects, achieving better FDR control and power in T2D data.

The paper introduces SuSiE2, a method that integrates eQTL information with fine mapping to improve causal variant identification in GWAS by leveraging expression data.