Lin Gao

scAtlasPy enables efficient analysis of large single-cell atlases by using disk-resident computing, achieving high speed and low memory usage compared to existing tools.

BandHiC reduces memory usage for Hi-C matrices by 99% using a banded storage strategy while enabling efficient analysis of ultra-high-resolution data.

The paper identifies and validates High-Purity Differential Expression (HiP-DEP) in spatial domains, showing it is more prevalent than bulk or single-cell data and applies it to cancer, Alzheimer's, and brain tissue analysis.

CellLoop identifies single-cell chromatin loops using a density-based framework, improving accuracy in mouse brain and embryogenesis data.

HRCHY-CytoCommunity identifies hierarchical multicellular structures in spatial cell-type maps and improves patient stratification in breast cancer.

This paper introduces VeloVGI, a method for accurate RNA velocity estimation in batch scRNA-seq data, outperforming existing methods in various datasets.

The paper develops HiCS, a method to identify hierarchical chromatin domains in single-cell Hi-C maps, revealing multiple factors and retrotransposons involved in boundary formation in mouse embryonic stem cells.

CytoTalk constructs cell type-specific signal transduction networks from single-cell RNA-Seq data and validates them using spatial transcriptomics and perturbation experiments.

The paper introduces a topology-based method to connect fragmented disease modules and demonstrates its application on curated diseases and multi-omics data.

This paper introduces STAM, a method using multi-layer networks to identify drug treatment patterns by analyzing tissue-specific PPI networks and drug-target modules.

The paper introduces MSTD, a method to detect multi-scale topological domains from 3D genomic maps, showing its effectiveness in identifying promoter-anchored interaction domains and improving accuracy over existing methods.