Daoliang Zhang

This study benchmarks preprocessing workflows for spatial transcriptomics data across multiple platforms and identifies platform-specific recommendations to improve downstream analysis.

GeneSPOT is a gene-centric framework that characterizes spatial gene relationships and their reorganization across biological conditions and time.

The paper introduces MAPS, a platform for unifying physical and molecular coordinates across modalities in spatial biology, demonstrating its effectiveness on diverse datasets and applications.

This paper introduces Histo3D-MO, a framework for reconstructing 3D spatial multi-omics maps at single-cell resolution, revealing spatial patterns in tumor microenvironments.

NicheTrans translates single-omics data into spatial multi-omics insights by integrating spatial and multi-modal information, outperforming existing methods in disease and tissue analysis.

This paper introduces SpaWeaver, a framework for robust spatial multi-omics integration using weak anchors, showing improved performance in handling real-world challenges and enabling new biological insights.

The paper presents a verified spatial transcriptomics atlas built from public data, showing that increasing study diversity improves model transfer and reproducibility.

PRISM improves spatial multi-omics analysis by imputing missing data and identifying spatial domains, outperforming existing methods on simulated and real datasets.

stGCL integrates multi-modal spatial transcriptomics data using graph contrastive learning to improve clustering and spatial pattern analysis.

stMMR identifies spatial domains from multi-modal spatial transcriptomics data using geometric deep learning, showing improved performance in domain identification and gene discovery across biological contexts.