Mei Li

This paper introduces a platform called INTACT that non-invasively monitors specific mRNA levels in vivo by using synthetic serum markers and RNA-based sensors.

CSRefiner improves cell segmentation accuracy with small datasets by fine-tuning existing models for spatial transcriptomics.

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

StereoMM integrates spatial transcriptomic data and histological images to improve spatial domain recognition and tumor heterogeneity analysis in cancer tissues.

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

CellBinDB provides a large-scale multimodal annotated dataset for cell segmentation and benchmarks seven state-of-the-art methods.

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.

spatiAlign integrates spatially resolved transcriptomics datasets using contrastive learning, improving batch effect removal and enabling downstream analysis across multiple tissue sections.

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