Wei Chen

eQTM Atlas, an R Shiny resource, curates over 11 million DNA methylation-gene expression associations from eight cohorts and 11 tissues to annotate EWAS CpGs beyond proximity.

The paper introduces a supervised learning algorithm for multi-phenotype genomic prediction, showing improved performance in gene expression and DNA methylation analysis and cell type deconvolution.

This paper introduces scRNAkinetics, a method to estimate RNA kinetics parameters from scRNA-seq data using biological priors, showing accurate inference on simulated datasets and validation with existing studies.

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.

This paper introduces RADO, a doublet detection algorithm for single-cell sequencing data that maximizes the area under the precision-recall curve, outperforming existing methods on multiple datasets.

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 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.

The paper identifies VNTRs near SLC9A3 associated with gene expression in CF nasal epithelia and shows their utility in explaining GWAS signals.

The paper introduces a region-based causal mediation analysis method for DNA methylation data, showing improved power for detecting mediated effects compared to single CpG site analysis.

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.

BAMM-SC clusters droplet-based scRNA-seq data from multiple individuals by accounting for batch effects and heterogeneity, improving accuracy over existing methods.