Cliburn C. W. Chan

This study compares acute infection and viral rebound dynamics in ART-treated infant rhesus macaques using a mathematical model to identify increased infected cell death during rebound.

This paper identifies a signature of anti-tumour activity in CD8+ T-cells across oral cancers using single-cell RNA and TCR sequencing, and develops a machine learning classifier to detect these cells from transcriptome data.

The paper develops a 64-plex single-HLA reporter cell panel and analysis tool to identify and characterize allogeneic HLA-specific B cells in sensitized individuals.

This study uses spatial transcriptomics to show that EBV+ HIV-associated NHLs have distinct tumor microenvironment features, including immunosuppressive pathways and spatial organization similar to lymphoid tissues.

DeepScence identifies senescent cells in single-cell and spatial transcriptomics data, outperforming existing methods.

This study identifies and characterizes Rhesus CMV-encoded Fcγ-binding glycoproteins, showing they delay antibody-mediated immune control during primary infection.

The nonhuman primate model mirrors the epidemiology of human congenital CMV (cCMV) after primary infection and reveals its transmission bottlenecks.

This study evaluates a triple bNAb combination in infant rhesus macaques, showing durable neutralization and effector functions with high predicted prevention efficacy against HIV strains.

B-Lightning identifies marker genes and cell subpopulations for specific sources of heterogeneity while filtering out confounding factors, improving sensitivity and robustness in single-cell data analysis.

This study examines how early ART initiation in HIV-infected infants affects antibody development and latent reservoir dynamics, showing their opposing impacts on post-treatment viral rebound.

SifiNet identifies gene sets and annotates cells without clustering, showing better performance than existing methods on multiomic data.

This paper introduces CDI, a new unsupervised measure to evaluate scRNA-seq clustering accuracy and select optimal parameters.

The paper introduces an ontology-guided approach for segmenting and identifying objects in developmental mouse lung immunofluorescent images, demonstrating its effectiveness using LungMAP data.