Joyce B. Kang

This study identifies granzyme K as a driver of a novel complement activation pathway independent of classical, lectin, or alternative pathways.

The paper introduces GeNA, a tool to identify genetic variants influencing cell state abundance in single-cell data, finding five loci associated with immune cell state shifts.

This study identifies 504 plasma proteins influenced by HLA genetic variation in 45,330 Europeans, revealing their roles in immune function and disease.

The paper develops SCENT, a method to create cell-type-specific enhancer-gene maps from single-cell multimodal data, identifying causal variants and genes for diseases.

The paper demonstrates that modeling single-cell perturbation states improves detection of reQTLs by accounting for cell-level heterogeneity in perturbation responses.

The paper maps cell-type-specific eQTLs for HLA genes across multiple tissues and identifies dynamic regulation of HLA-DQ genes in immune cells.

This paper provides a statistical genetics guide for imputing HLA alleles from genotype data and offers best practices for association testing to identify HLA variants driving complex diseases.

The paper uses a single-cell multiome atlas of the human retina and deep learning to identify causal noncoding variants in eye diseases.

The paper deconstructs RA synovium using single-cell data to identify six cell type abundance phenotypes associated with distinct inflammatory states.

Symphony maps query cells to large reference atlases efficiently, enabling rapid identification of cell states and phenotypes across diverse datasets.

This paper introduces CNA, a method to identify disease-associated cell states by analyzing transcriptional neighborhoods across samples, showing improved performance over cluster-based approaches in simulations and real datasets.

The paper introduces scPOST, a tool for estimating statistical power in single-cell studies, and shows that larger sample sizes, reduced batch effects, and lower cell state variability increase power to detect clinically associated cell state shifts.

This study uses read cloud sequencing to identify mobile elements in Bacteroides caccae that contribute to antibiotic resistance in a patient's microbiome over time.