Jinpu Li

This paper introduces STORM, a statistical framework for analyzing spatial patterns in multi-omics data, demonstrating reliable performance in detecting spatial structures and supporting study design decisions.

This paper introduces scPEFT, a parameter-efficient fine-tuning framework for scLLMs that improves task adaptation with reduced computational costs and enhances performance in disease-specific and cross-species analyses.

TrimNN identifies spatial cell organization patterns from spatial transcriptomics and proteomics data to reveal associations between cell-type arrangements and phenotypes.

scBSP identifies spatially variable genes from spatial transcriptomic data efficiently and accurately across different sequencing techniques and resolutions.

This paper introduces BSP, a method for identifying spatially variable genes from 2D or 3D spatial transcriptomics data, showing improved accuracy and efficiency in simulations and biological validations.

DeepMAPS infers biological networks from single-cell multi-omics data using a heterogeneous graph transformer, outperforming existing tools in cell clustering and pathway prediction.