Xuegong Zhang

The paper introduces eQTLsingle, a method to discover single-cell eQTLs using only scRNA-seq data, revealing cell-type-specific tumor-related eQTLs not found in bulk studies.

DeepCAGE predicts chromatin accessible regions using deep learning, integrating sequence data and transcription factor activity, and identifies disease-associated variants.

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

SOMDE identifies spatially variable genes in large-scale spatial transcriptomic data efficiently using self-organizing maps and Gaussian Processes.

HGC enables efficient multiresolution clustering of large-scale single-cell data by combining graph-based and hierarchical clustering methods, achieving state-of-the-art accuracy.

OpenAnnotate provides a web server for efficiently annotating chromatin accessibility of genomic regions across biosamples, tissues, and systems.

SCeQTL is an R package that identifies eQTL from single-cell sequencing data using zero-inflated negative binomial regression to detect gene-expression differences across genotype groups and other factors.

The paper introduces a new statistic for efficiently detecting repetitive sequences in genomic data and demonstrates its effectiveness across multiple scenarios.

The paper identifies key genomic and epigenomic signatures of super-enhancers and their constituents through integrative analysis and computational modeling.

This study reproduces a human cell atlas publication on blood dendritic cells to assess reproducibility in single-cell RNA-seq analysis and identifies factors affecting results.

The paper shows miRNAs reduce gene expression noise in mouse cells using single-cell RNA-sequencing data analysis.

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

scMetric provides an R package for metric learning and visualization of single-cell RNA-seq data to improve clustering and dimensionality reduction.

The paper compares super-enhancers and stretch enhancers across ten human cell types, finding super-enhancers are more transcriptionally active and cell-type-specific.

The paper analyzes RNA-Seq data to characterize kinase gene expression and splicing in prostate cancer, identifying isoform switching in CDK5 that affects AR phosphorylation.

The paper identifies early- and late-switch exons in neural development and shows they are regulated by RNA-binding proteins, with some neuronal subtypes lacking these regulators and retaining an immature splicing program.

DEsingle identifies three types of differential expression in single-cell RNA-seq by distinguishing real and dropout zeros using a Zero-Inflated Negative Binomial model.

dbSUPER is a database of super-enhancers in mouse and human genomes, providing integrated resources for research on transcriptional control and disease.