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Scientific Reports|October 21, 2017
Controlling for Confounding Effects in Single Cell RNA Sequencing Studies Using both Control and Target GenesMengjie Chen, Xiang ZhouGenome Biology|November 14, 2018
VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studiesMengjie Chen, Xiang ZhouNucleic Acids Research|December 6, 2021
Effective and scalable single-cell data alignment with non-linear canonical correlation analysisJialu Hu, Mengjie Chen, Xiang ZhouGenome Biology|August 9, 2020
Demystifying "drop-outs" in single-cell UMI dataTae Hyun Kim, Xiang Zhou, Mengjie ChenBiorxiv : the Preprint Server for Biology|June 10, 2024
The curses of performing differential expression analysis using single-cell dataChih-Hsuan Wu, Xiang Zhou, Mengjie ChenGenome Biology|March 18, 2025
Exploring and mitigating shortcomings in single-cell differential expression analysis with a new statistical paradigmChih-Hsuan Wu, Xiang Zhou, Mengjie ChenHuman Molecular Genetics|September 11, 2019
SNV identification from single-cell RNA sequencing dataPatricia M Schnepp, Mengjie Chen, Evan T Keller, et al.Bioinformatics (Oxford, England)|July 19, 2018
Heritability estimation and differential analysis of count data with generalized linear mixed models in genomic sequencing studiesShiquan Sun, Jiaqiang Zhu, Sahar Mozaffari, et al.Bioinformatics (Oxford, England)|October 9, 2018
CoMM: a collaborative mixed model to dissecting genetic contributions to complex traits by leveraging regulatory informationCan Yang, Xiang Wan, Xinyi Lin, et al.Nature Communications|March 30, 2020
Integrative differential expression and gene set enrichment analysis using summary statistics for scRNA-seq studiesYing Ma, Shiquan Sun, Xuequn Shang, et al.Pageof 339