Jiashun Xiao

Hierarchical distribution matching enables comprehensive characterization of common and condition-specific cell niches in spatial omics data

This paper introduces Harmonics, a framework for identifying common and condition-specific cell niches in spatial omics data, demonstrating its effectiveness across diverse datasets and diseases.

CellART: a unified framework for extracting single-cell information from high-resolution spatial transcriptomics

CellART extracts single-cell information from high-resolution spatial transcriptomics data across multiple platforms, demonstrating efficiency and accuracy in cancer datasets.

Accurate and scalable decontamination of imaging-based spatial transcriptomics via optimal transport

CellDot improves spatial transcriptomics by accurately decontaminating transcript assignments using optimal transport, enhancing biological interpretation and spatial pattern recovery.

traceCB: Trans-ancestry cell-type-specific eQTLs mapping by integrating scRNA-seq and bulk data

traceCB improves cell-type-specific eQTL mapping by integrating scRNA-seq and bulk data across ancestries, increasing eGene discovery and replication rates.

MRMU: A New Paradigm for Mendelian Randomization by Accounting for Measured Covariates and Unmeasured Confounders

MRMU improves Mendelian randomization by accounting for measured covariates and unmeasured confounders, identifying robust risk factors for coronary artery disease and novel drug targets.

STAT: A multi-agent framework for integrated and interactive spatial transcriptomics analysis

STAT is a multi-agent framework that improves spatial transcriptomics analysis by enabling interactive, transparent, and efficient multi-task analysis across different data resolutions.

NicheScope: Identifying Multicellular Niches and Niche-Regulated Cell States in Spatial Transcriptomics

NicheScope identifies multicellular niches and their regulated cell states from spatial transcriptomics data, revealing tissue structures and clinically relevant tumor niches.

A unified framework for cell-type-specific eQTLs prioritization by integrating bulk and scRNA-seq data

This paper introduces IBSEP, a statistical framework that integrates bulk and scRNA-seq data to prioritize cell-type-specific eQTLs, showing improved performance over existing methods.

UCS: a unified approach to cell segmentation for subcellular spatial transcriptomics

UCS improves cell segmentation for subcellular spatial transcriptomics by integrating nuclei and transcript data, enhancing accuracy and enabling downstream analyses.

Funmap: integrating high-dimensional functional annotations to improve fine-mapping

Funmap improves fine-mapping by integrating high-dimensional functional annotations, achieving better power and controlled false positive rates compared to existing methods.

A Robust Statistical Approach for Finding Informative Spatially Associated Pathways

The paper introduces a statistical method to identify spatially associated biological pathways, finding significant pathways linked to spatial variation in human and mouse datasets.

Benchmarking Mendelian Randomization methods for causal inference using genome-wide association study summary statistics

This study benchmarks 15 Mendelian Randomization methods using real genetic data to evaluate their performance in causal inference under various confounding scenarios.

XMAP: Cross-population fine-mapping by leveraging genetic diversity and accounting for confounding bias

XMAP improves fine-mapping by using cross-population GWAS data and accounting for confounding bias, enhancing statistical power and computational efficiency.

SpatialScope: A unified approach for integrating spatial and single-cell transcriptomics data using deep generative models

SpatialScope integrates spatial and single-cell transcriptomics data using deep generative models to achieve single-cell resolution and infer transcriptome-wide expression levels.

Organoid-based single-cell spatiotemporal gene expression landscape of human embryonic development and hematopoiesis

The paper defines the single-cell spatiotemporal gene expression landscape of human embryonic development using stem-cell-derived organoids and identifies key developmental signatures and signaling pathways.

Leveraging the local genetic structure for trans-ancestry association mapping

This paper introduces LOG-TRAM, a method to improve trans-ancestry association mapping by leveraging local genetic structure, enhancing power and reducing bias in under-represented populations.