Chun Ye

This paper introduces SURGE, a method to discover context-specific eQTLs from single-cell RNA-seq data, showing its relevance in disease through colocalization analysis.

The paper introduces memento, a method for differential expression analysis of scRNA-seq data, and shows it identifies significant differences in mean, variability, and gene correlation across experimental conditions.

The paper identifies cell-type and cell-subtype specific eQTLs in aging human cortex, revealing variant effects on gene expression and disease associations.

The paper introduces CONTENT, a method for multi-context genetic modeling of transcriptional regulation, which improves power and discovers more gene-phenotype associations in TWAS.

This paper uses a deep protein language model to predict the functional impact of all human missense variants, outperforming existing methods and providing isoform-specific annotations.

This study shows that interneurons continue to migrate into the human entorhinal cortex postnatally, with distinct lineage contributions and maturation patterns identified using single-nuclei RNAseq.

SCITO-seq enables cost-effective profiling of over 10^5 cells using combinatorial indexing with dsc-seq, demonstrating feasibility in mixed cell lines and human lymphocytes.

The paper uses single-cell RNA-sequencing to show that gene expression in immune cells is influenced by genetic variation and pathogen exposure, revealing context-specific regulatory mechanisms.

The paper uses single-cell RNA sequencing to analyze murine pancreatic duct cells, identifying heterogeneity and functional roles in disease progression.

The paper evaluates whether donor-multiplexed scRNA-seq introduces alloreactive gene expression changes and finds no detectable alloreactivity across three workflows.

This study shows that low-coverage single-cell RNA sequencing of more samples increases statistical power for cell-type-specific eQTL analysis compared to high-coverage sequencing of fewer samples.

This study identifies ethnicity-specific transcriptomic subgroups in SLE patients using RNA-seq data and correlates them with disease activity.

The paper tests gene expression prediction models' performance across populations and finds poor generalizability to African Americans compared to European Americans.

The paper introduces DIEM, a semi-supervised machine learning classifier to remove debris-contaminated droplets in snRNA-seq, improving clustering quality across three datasets.

VISION annotates variation sources in single-cell RNA-seq data without preconceived group stratification, linking findings to clinical phenotypes in lupus B cells.

The paper uses CRISPR perturbations and single-cell transcriptomics to identify gene regulators and interactions in CD4+ T cells, revealing 134 regulators and 194 regulator pairs.

The paper identifies genetic variants influencing isoform usage in the anti-viral response, revealing ERAP2's role in balancing selection and influenza-specific regulation.

This study identifies genetic risk loci for narcolepsy, showing enrichment in immune cells and suggesting autoimmune modulation via T cell receptor chains and influenza-A infection.

The paper identifies genetic variants affecting chromatin accessibility in T cells and links them to gene expression and autoimmune disease.

The paper introduces demuxlet, an algorithm that uses natural genetic variation to multiplex single-cell RNA-seq samples, enabling accurate sample identification and doublet detection in pooled experiments.

The paper identifies an IL2RA enhancer associated with autoimmunity using CRISPRa and shows it affects gene activation timing without blocking expression.