Sean Jordan

Multiple myeloma (MM) displays significant genetic heterogeneity, making it challenging to distinguish malignant from non-malignant plasma cells in single-cell datasets. Existing marker-based and CNV detection methods require manual intervention or high computational resources, limiting their scope. We developed a supervised deep learning autoencoder to classify malignant cells across MM and its precursor stages and validated its performance and biological relevance. The model outperformed...

CONCLUSIONS: By integrating these innovative features, with specialized survival analyses on single-cell cluster markers, co-expression modules, regulatory networks, and ligand-receptor pairs, Survival Genie 2 represents a novel tool for expanding the translational impact of cancer research, enabling the precise identification of biomarkers and therapeutic targets. Survival Genie 2 is available at https://bhasinlab.bmi.emory.edu/SurvivalGenie2/home.

Multiple myeloma (MM) is a plasma cell malignancy shaped by dynamic interactions between MM cells and non-malignant cells in the immune microenvironment. To spatially profile the influence of cellular context on MM and immune cell expression, we developed a multimodal framework integrating 10x Genomics Visium HD, 10x Genomics Xenium, and clinically annotated single-cell RNA (scRNA-seq) sequencing datasets. Visium HD enabled unbiased, whole transcriptome, spatial discovery at 16 µm resolution,...

BACKGROUND: The role of adipokines in childhood glycemia is poorly understood. We investigate the longitudinal association between adipokines and glycemia in a cohort of children in Mexico City.