Yunqing Liu

This study introduces RVMMAT, a method for detecting time-varying genetic effects on longitudinal binary traits, and applies it to identify hypertension-associated loci.

SDePER improves cell type deconvolution in spatial transcriptomic data by addressing platform effects, sparsity, and spatial correlation, achieving more accurate results than existing methods.

iDESC identifies differential expression in scRNA-seq data with multiple subjects by separating subject effects from disease effects, showing better performance than existing methods.

This study introduces two statistical tests to detect gene-environment interactions in hypertension, identifying two genes interacting with alcohol use.

G2S3 improves single-cell RNA sequencing data by imputing dropouts using a gene graph, enhancing expression recovery and analysis.