Xuesen Cheng

To be or not to be - Decoding the Trabecular Meshwork Cell Identity

This study performs single-cell RNA sequencing on primary human trabecular meshwork cells in vitro to compare gene expression with in situ tissue, providing a public resource for studying cellular identity and translatability.

Comprehensive single-cell atlas of the mouse retina

The paper generates a large-scale single-cell RNA-seq dataset of the mouse retina and integrates it with public data to create a comprehensive cell atlas.

Integrated multi-omics single cell atlas of the human retina

The paper creates a comprehensive human retina cell atlas using single-cell multi-omics data, revealing over 110 cell types and their gene regulatory networks, and enables fine-mapping of GWAS and eQTL variants.

DeMixSC: a deconvolution framework that uses single-cell sequencing plus a small benchmark dataset for improved analysis of cell-type ratios in complex tissue samples

DeMixSC improves deconvolution accuracy by using single-cell RNA-seq and a benchmark dataset to account for technological discrepancies in complex tissue samples.

A multi-omics atlas of the human retina at single-cell resolution

This paper presents a comprehensive single-cell multi-omics atlas of the human retina, integrating RNA-seq and ATAC-seq data to identify cell-type-specific cis-element-gene relationships and transcription factor regulons.

Monopogen: single nucleotide variant calling from single cell sequencing

Monopogen calls single nucleotide variants from single-cell sequencing data, achieving high accuracy in germline variant detection and enabling ancestry inference and variant association analysis.

Integrative single cell multiomics analysis of human retina indicates a role for hierarchical transcription factors collaboration in genetic effects on gene regulation

The paper identifies cell type-specific genetic effects on gene regulation in human retina using single-cell multiomics data and finds that trans-factors, not cis-elements, drive these effects.

Unbiased integration of single cell multi-omics data

bindSC integrates single-cell multi-omics data accurately by aligning rows and columns without approximations, outperforming existing methods in various biological contexts.