Cuining Liu

This study examines epigenomic and spatial changes during human reprogramming, finding distinct molecular and spatial patterns in productive versus unsuccessful trajectories.

FastGxC efficiently maps context-specific eQTLs in bulk and single-cell RNA-seq data, improving power and speed while enhancing biological relevance and trait association precision.

The paper identifies distinct DNA methylation mechanisms for common and rare noncoding genetic risk in brain disorders using deep learning and single-nucleus DNAm data.

The paper presents a single-cell DNA methylation and transcriptomics analysis of ASD, identifying cell type-specific methylation changes and age-related effects in the brain.

The paper integrates polygenic risk scores with single-cell methylation data to identify methylation-driven disease relevance in depression and other traits.

The paper evaluates demultiplexing methods for joint snRNA/snATAC experiments under ambient contamination and introduces a new metric to assess their performance.

The paper integrates DNA methylation, mRNA, and miRNA data from bronchoalveolar lavage cells to identify molecular signatures of sarcoidosis and disease progression.

This paper introduces a new rare-variant association test that incorporates family history information, showing improved performance over standard methods in simulated data and a study of cleft lip and palate.