James Shiniti Nagai

AtlasOT improves multimodal integration of disease atlases by addressing unpaired data constraints through a novel optimal transport framework.

GraphHDBSCAN* performs hierarchical clustering on scRNA-seq data, recovering biologically meaningful hierarchies and improving flat partition quality.

This study identifies Spp1⁺ macrophages as key hubs coordinating fibrotic remodeling in myeloproliferative neoplasms using NicheSphere, a novel computational method for cell communication analysis.

The paper shows that Wnt-dependent reprogramming of bone marrow niches drives fibrosis by activating osteolineage cells and altering hematopoietic support.

PILOT-GM-VAE analyzes single cell disease atlases to perform sample-level clustering, trajectory inference, and batch correction using Gaussian Mixture Variational Autoencoders with optimal transport.

scACCorDiON clusters patient-specific cell-cell communication graphs using optimal transport, improving disease status prediction in scRNA-seq data.

PHLOWER infers complex differentiation trees from multi-modal single-cell data using Hodge Laplacian decomposition, showing effectiveness in kidney organoid data.

PILOT uses optimal transport to analyze single-cell and pathomics data, detecting disease-associated samples and non-linear changes in cell populations and tissue structures.

scMEGA infers gene regulatory networks from single-cell multi-omics data, demonstrating its application in studying myofibroblast activation in human myocardial infarction.

This study generates a high-resolution spatial multi-omic map of human myocardial infarction by integrating single-cell gene expression, chromatin accessibility, and spatial transcriptomic data to reveal cellular zones of injury, repair, and remodeling.

The paper compares 15 resources and 6 methods for inferring cell-cell communication from single-cell RNA data, finding significant variation in predicted interactions and biological interpretations.

This study uses human pluripotent stem cell-derived kidney organoids to model nephrotic syndrome, showing they can replicate podocyte injury and respond to genetic correction.

This study profiles the kinomes of sugarcane and sorghum, identifying PKs and their evolutionary relationships, and provides expression patterns and coexpression networks.

CrossTalkeR analyzes and visualizes ligand-receptor networks to identify changes in cell communication between two biological phenotypes using single-cell RNA-seq data.

The paper uses machine learning to identify genomic regions associated with sugarcane brown rust resistance by analyzing SNP data from full-sib progeny.