Jia Zhao

Spatial co-expression and cell-cell communication inference from spatially resolved transcriptomics with CONCISE

CONCISE improves spatial ligand-receptor interaction inference by accounting for spatial autocorrelation and measurement errors, reducing false positives and enhancing detection power in spatial transcriptomics data.

Differential developmental blueprints of organ-intrinsic nervous systems

The paper compares the developmental strategies of the enteric nervous system and intrinsic cardiac nervous system to reveal how organ-specific specialization occurs through distinct molecular and cellular mechanisms.

INSPIRE: interpretable, flexible and spatially-aware integration of multiple spatial transcriptomics datasets from diverse sources

INSPIRE integrates and interprets multiple spatial transcriptomics datasets, revealing tissue structures and biological processes across diverse samples and technologies.

scGPD: single-cell informed gene panel design for targeted spatial transcriptomics

scGPD designs gene panels for spatial transcriptomics using single-cell data, achieving better coverage and classification accuracy than existing methods.

Improving polygenic risk prediction performance through integrating electronic health records by phenotype embedding

This paper introduces EEPRS, a framework that improves polygenic risk prediction by integrating electronic health records through phenotype embeddings, showing significant performance gains over traditional methods.

scMODAL: A general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links

scMODAL is a deep learning framework for aligning single-cell multi-omics data using feature links, effectively removing unwanted variation and identifying cell subpopulations.

Knockoff procedure improves causal gene identifications in conditional transcriptome-wide association studies

This paper introduces TWASKnockoff, a method that improves causal gene identification in TWAS by accounting for gene correlations and genetic variant effects, achieving better FDR control and power in T2D data.

Modeling and predicting single-cell multi-gene perturbation responses with scLAMBDA

scLAMBDA models and predicts single-cell transcriptional responses to genetic perturbations, outperforming existing methods in accuracy and generalization.

Beyond Integration: SuperGLUE Facilitates Explainable Training Framework for Multi-modal Data Analysis

This paper introduces a scalable probabilistic deep learning framework for multi-modal data integration, demonstrating improved preservation of local and global structures in complex biological systems.

Construction of a 3D whole organism spatial atlas by joint modeling of multiple slices

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

Mendelian Randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics

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

Adversarial domain translation networks for fast and accurate integration of large-scale atlas-level single-cell datasets

Portal integrates large-scale single-cell datasets efficiently and accurately by preserving biological variation across samples, platforms, and species.

FIRM: Flexible Integration of single-cell RNA-sequencing data for large-scale Multi-tissue cell atlas datasets

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

Tabula Microcebus: A transcriptomic cell atlas of mouse lemur, an emerging primate model organism

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.