Zhiyuan Yuan

DeSPA uncovers genes with cross-state spatial pattern remodeling and coordinated spatial programs across multi-slice spatial transcriptomics atlases

DeSPA identifies genes with spatial pattern remodeling across biological states and reveals coordinated spatial programs in multi-slice spatial transcriptomics data.

Platform-specific count-matrix preprocessing workflows for spatial transcriptomics data analysis

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

Deciphering the gene-centric spatial dynamics across tissue and time with GeneSPOT

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

Unifying physical and molecular coordinate systems across modalities in spatial biology

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.

AI-enabled reconstruction of 3D spatial multi-omics at single-cell resolution

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: Spatial-aware Cross-omics Translation

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.

Robust integration of weakly anchored spatial multi-omics

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.

Decoding Hierarchical Cell-Cell Communication in Spatial Multi-Omics with CellSTIC

CellSTIC decodes hierarchical cell-cell communication in spatial multi-omics data by integrating multimodal evidence and organizing interactions into a hierarchical semantic representation.

An agent-built, verified atlas of public spatial transcriptomics shows that breadth across studies drives transfer and reproducibility

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

SOFisher: Reinforcement Learning-Guided Experiment Designs for Spatial Omics

SOFisher improves spatial omics experiment design by using reinforcement learning to guide FOV selection, outperforming conventional methods in capturing relevant biological features.

H2O: A Foundation Model Bridging Histopathology to Spatial Multi-Omics Profiling

H2O predicts spatial transcriptomics and proteomics from H&E images using a contrastive learning framework, outperforming existing models and recovering biologically relevant cell-cell communication.

A lightweight, ultrafast and general embedding framework for large-scale spatial omics data

This paper introduces SpaLP, a framework for spatial omics data that outperforms existing methods in efficiency and accuracy across multiple tasks.

PRISM: Niche-informed Deciphering of Incomplete Spatial Multi-Omics Data

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

Bridging the Dimensional Gap from Planar Spatial Transcriptomics to 3D Cell Atlases

SpatialZ generates dense 3D cell atlases from planar spatial transcriptomics data by reconstructing virtual slices, validated with real 3D data and enabling new spatial analyses.

Unveiling Tissue Structure and Tumor Microenvironment from Spatial Omics by Hypergraph Learning

HyperSTAR identifies spatial domains across resolutions using hypergraph learning, outperforming existing methods in spatial omics analysis and revealing breast cancer spatial heterogeneity.

High-Parameter Spatial Multi-Omics through Histology-Anchored Integration

This paper introduces SpatialEx and SpatialEx+ frameworks to integrate spatial multi-omics data using histology as an anchor, achieving high-parameter co-profiling across tissue sections.

Predicting Spatial Transcriptomics from H&E Image by Pretrained Contrastive Alignment Learning

This paper introduces CarHE, a method that predicts spatial transcriptomics data from H&E images using contrastive learning, achieving high accuracy and enabling cost-effective analysis.

Stereopy: modeling comparative and spatiotemporal cellular heterogeneity via multi-sample spatial transcriptomics

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

Identifying Differential Spatial Expression Patterns across Different Slices, Conditions and Developmental Stages with Interpretable Deep Learning

The paper introduces River, an interpretable deep learning method to identify differential spatial expression patterns across slices, conditions, and developmental stages, validated on diverse biological datasets.

Scalable Condition-relevant Cell Niche Analysis of Spatial Omics Data with Taichi

Taichi identifies condition-relevant cell niches in spatial omics data by analyzing intra-slice heterogeneity across multi-slice and multi-condition datasets.

stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multi-modal feature representation

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.

Spatially Resolved Gene Expression is Not Necessary for Identifying Spatial Domains

This study introduces BINARY, a graph convolutional network using binarized spatial transcriptomics data to identify spatial domains, outperforming existing methods with less input.

Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding

Spatial-ID improves cell type annotation in spatial transcriptomics by combining transfer learning and spatial embedding, outperforming existing methods on public datasets.