Zhongli Xu

Methyl-TWAS improves gene expression imputation and TWAS by using long-range DNA methylation markers, outperforming existing methods in nasal epithelium and white blood cells.

The paper identifies three nasal DNA methylation CpG sites that predict childhood allergic disease with high accuracy, validated across Dutch and US Hispanic children.

SECANT improves clustering and annotation of single-cell multi-omics data by integrating surface protein and RNA information with biology-guided semi-supervised learning.

This paper introduces TransformerST, a Transformer-based model for spatial transcriptomics analysis without single-cell references, achieving high-resolution tissue clustering and gene expression prediction.

This study integrates spatial transcriptome, single-cell RNA-seq, and single-cell ATAC-seq data from mouse lungs after immunization to identify cell types and their spatial dynamics.

This study benchmarks DOGMA-seq and CITE-seq in human T cells, finding DOGMA-seq provides more information with optimized permeabilization despite slightly lower mRNA and ADT library quality.

This paper introduces GMM-Demux, a method to identify and remove multiplets in scRNA-seq data, showing improved accuracy and error reduction.

The paper identifies methylation-gene expression associations in nasal epithelium from Puerto Rican children with and without asthma, revealing epigenetic regulation of gene expression in atopic asthma.

BREM-SC is a Bayesian model that jointly clusters single-cell transcriptomic and proteomic data, improving accuracy and quantifying clustering uncertainty.