Eric Sun

CellVoyager autonomously analyzes scRNA-seq data to generate new hypotheses and findings, outperforming existing models and producing scientifically interesting results.

BrainBridge integrates single-cell transcriptomic data with phenotypes to model brain biology, prioritizing genes and cell types linked to Alzheimer's, aging, and sex differences.

SpatialProp predicts the effects of multi-gene perturbations on whole tissue sections using spatially resolved single-cell transcriptomics and graph neural networks.

The paper develops spatial aging clocks to analyze cell proximity effects in mouse brain aging and rejuvenation, revealing pro-aging and pro-rejuvenating influences of specific cell types.

SPRITE improves spatial gene expression predictions by integrating gene networks and spatial relationships, enhancing clustering and classification in spatial transcriptomics data.

TISSUE improves spatial transcriptomics analysis by providing uncertainty-calibrated predictions, enhancing downstream tasks like clustering and differential expression analysis.

This paper introduces SMAI, a framework for alignability testing and integration of single-cell data, showing it outperforms existing methods and improves downstream analyses.

The paper investigates chromatin accessibility changes in neurogenic niche cells during aging, identifying defects in NSC adhesion and migration and proposing ROCK inhibition as a potential therapeutic strategy.

The paper introduces a spectral method to evaluate and combine multiple data visualizations, improving the quality of the consensus visualization by leveraging eigenscores.

DynamicViz generates dynamic visualizations to assess the reliability of dimensionality reduction methods by quantifying dynamic variability in data.

This study develops cell type-specific aging clocks using single-cell transcriptomic data to quantify aging and rejuvenation in the brain's neurogenic regions.