Can Yang

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

PerturbBridge predicts single-cell perturbation responses by modeling population-level distribution shifts using a conditional latent Schrödinger Bridge framework, achieving state-of-the-art differential-expression recovery.

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

This paper introduces stGP, a statistical framework to identify cell-type-specific spatiotemporal gene programs, revealing how gene programs are influenced by time and spatial structure in tissues.

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

XMR improves causal inference in underrepresented populations by using cross-population GWAS summary statistics to increase instrumental variables and enhance statistical power.

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

Genomic Touchstone evaluates genomic language models across 36 tasks and 88 datasets, revealing insights on model performance, scaling, and pretraining strategies.

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

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

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

This paper introduces STitch3D, a framework that reconstructs 3D spatial structures from multiple 2D tissue slices, enabling comprehensive 3D analysis of tissues and organisms.

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

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.

This paper uses a mouse lemur transcriptomic atlas to identify new genes, splice junctions, and disease-related features, highlighting their relevance to primate biology and human disease.

This paper introduces MR-APSS, a Mendelian Randomization method that addresses pleiotropy and sample structure to improve causal inference from GWAS summary statistics.

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.

FIRM integrates heterogeneous scRNA-seq datasets across tissues and platforms, accurately preserving cell type identities and structure.

The paper creates a comprehensive cell atlas of mouse lemurs using single-cell RNA sequencing, identifying over 750 cell types and comparing their molecular profiles to humans, mice, and macaques.

This paper introduces TisCoMM, a tissue-specific collaborative mixed model for TWAS that improves gene-trait association detection and identifies tissue-specific gene roles in complex traits.

This paper introduces PMR-Egger, a probabilistic Mendelian randomization method to test and control horizontal pleiotropy in transcriptome-wide association studies, showing improved type I error control and power.

This paper introduces MR-LDP, a Mendelian randomization method that accounts for linkage disequilibrium and horizontal pleiotropy, showing improved causal inference in GWAS summary statistics.

IGREX quantifies the impact of genetically regulated expression on phenotypes using GWAS data and gene expression reference panels, showing significant phenotypic variation explained by GREX effects.

This paper introduces a latent probit model to analyze relationships among complex traits using GWAS summary statistics and functional annotations, showing improved performance over existing methods.

CoMM-S2 uses GWAS summary statistics to infer mechanistic roles of genetic variants in gene expression, achieving performance comparable to methods using individual-level data.

This paper introduces a statistical method to detect cell-type-specific risk-CpG sites in epigenome-wide association studies, improving detection power and enabling cell-type resolution.

This paper introduces AnnoPred, a framework that uses functional annotations to improve genetic risk prediction for complex diseases, showing enhanced accuracy across multiple diseases.

AC-PCA reduces dimensionality and adjusts for confounding variation in biological datasets, recovering anatomical structure and shared variation across species.