Shuangsang Fang

DCAF improves single-cell perturbation simulation by addressing data, evaluation, and model validity, outperforming baselines in generalization and recapitulating regulatory heterogeneity in mouse brain.

OmniCell integrates single-cell and spatial transcriptomics to model tissue context, revealing how gene programs operate in different anatomical environments.

RegFormer improves single-cell RNA sequencing analysis by integrating gene regulatory networks with Mamba-based modeling, outperforming existing models in clustering, integration, and annotation.

CellBin processes spatial omics data to create cell-level matrices, outperforming existing methods across multiple platforms and omics types.

DynPerturb models spatiotemporal perturbation dynamics in single-cell and spatial transcriptomics, outperforming existing methods in regulatory link prediction and uncovering dynamic regulatory rewiring in biological systems.

BioLLM integrates and benchmarks single-cell foundation models, revealing scGPTs' robust performance and highlighting limitations of scBERT in gene-level tasks.

UniCell improves cell type annotation and cross-dataset harmonization by combining Cell Ontology with transcriptomic data, outperforming existing tools in accuracy and rare cell detection.

Stereopy provides a framework for analyzing multi-sample spatial transcriptomics data to model comparative and spatiotemporal cellular heterogeneity through three key algorithms.

SpaGRN predicts spatially informed gene regulatory networks from spatial transcriptomics data, revealing spatiotemporal regulatory patterns in Drosophila development.

ST-GEARS improves spatial information recovery in 3D spatial transcriptomics by aligning sections and correcting distortions, enhancing accuracy for downstream analysis.

The paper updates a software tool to generate single-cell spatial gene expression profiles using cell boundary images from Stereo-seq data, showing improved performance on mouse liver and Arabidopsis seed datasets.

SAW improves the efficiency and accuracy of Stereo-Seq spatial transcriptomics analysis by optimizing key steps and reducing runtime.

BatchEval Pipeline evaluates batch effects in multi-dataset transcriptomics analysis and provides a report to guide batch correction.

CellBin processes high-resolution spatial transcriptomics data to generate accurate single-cell gene expression profiles, improving accuracy and efficiency in mouse brain tissue analysis.