Yang Ni

This paper introduces SCR, a Bayesian framework for estimating spatially-resolved gene co-expression networks to identify spatially co-expressed genes as potential biomarkers and therapeutic targets.

distQTL identifies distribution QTLs using population-scale single-cell RNA sequencing data, outperforming traditional methods in capturing transcriptional regulatory heterogeneity.

This paper introduces a method using Chatterjee's rank correlation to construct gene regulatory networks from scRNA-seq data, outperforming existing methods in capturing complex dependencies.

This paper introduces GraphR, a Bayesian method for probabilistic graphical modeling under sample heterogeneity, showing improved performance in network estimation and computational efficiency.

This paper introduces PRS-CS, a Bayesian polygenic prediction method that improves accuracy over existing approaches for complex diseases and traits.

The paper shows that variance in estimated genetic distance differs between whole haplotype sampling and independent locus sampling, influenced by linkage disequilibrium.