Wei Zhang

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.

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.

Deconer evaluates reference-based cell type deconvolution methods using simulated and real gene expression data, comparing 14 methods across multiple criteria and providing guidance for users.

This paper introduces scDEC-Hi-C, a deep generative framework for analyzing single cell Hi-C data, showing improved clustering and imputation over existing methods.

ARIC estimates cell type proportions from bulk gene expression or DNA methylation data, showing accuracy in both data types.

DISMIR detects cancer using cfDNA methylation and sequence data with high accuracy at low sequencing depth.

This study identifies ribosomal DNA methylation as a stable biomarker for detecting cancer in plasma with high accuracy across multiple cancer types.

SIP provides an interchangeable pipeline for scRNA-seq data processing, enabling easy integration and customization of analysis steps.