Tiejun Li

CytoBridge reconstructs continuous virtual cell dynamics from sparse spatial transcriptomic data across development, regeneration, and neurodegeneration.

STITCH reconstructs high-resolution spatial transcriptomic atlases by imputing missing data in 2D and 3D spatial transcriptomics datasets.

stVCR reconstructs spatiotemporal dynamics from time-series spatial transcriptome data by integrating generative deep learning with optimal transport and spatial priors.

Storm infers RNA velocity using metabolic labeling data by solving stochastic differential equations, revealing time-dependent transcriptional regulations in transient processes.

This paper develops algorithmic methods to improve RNA velocity analysis by addressing time-scale fixation, uncertainty quantification, bandwidth selection, and temporal distance estimation.

MuTrans identifies transition cells and quantifies cell-state transitions using multiscale stochastic dynamics, applied to diverse single-cell datasets.

The paper introduces SCORE, a network-based method that improves single-cell cellular state inference by incorporating molecular network features, identifying novel cell clusters and biological insights in human ileal scRNA-seq data.

This paper provides a mathematical analysis of the RNA velocity model, deriving analytical solutions and parameter inference frameworks to improve understanding and future development of RNA velocity methods.

The paper compares single-cell potency measures using 9 experiments and 6,600 RNA-Seq profiles, finding that graph entropy of a Markov Chain on a signaling network better estimates potency than other methods.