We develop statistical methods for genomics data analysis,
with a recent focus on single cell and cell-type-specific analysis.
 B. Zhu, H. Li, L. Zhang, S. S. Chandra, H. Zhao (2022) A Markov random field model-based approach for differentially expressed gene detection from single-cell RNA-seq data. Briefings in Bioinformatics, 23: bbac166.
 Y. Wang, T. Liu, H. Zhao (2022) ResPAN: a powerful batch correction model for scRNA-seq data through residual adversarial networks. Bioinformatics, 30: 3942-3949.
 D. Tang, S. Park, H. Zhao (2022) SCADIE: simultaneous estimation of cell type proportions and cell type-specific gene expressions using SCAD-based iterative estimating procedure. Genome Biology, 23: 129.
 Y. Wang, H. Zhao (2022) Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders. PLOS Computational Biology, 18: e1010025.
 H. Li, B. Zhu, Z. Xu, T. Adams, N. Kaminski, H. Zhao (2021) A Markov random field model for network-based differential expression analysis of single-cell RNA-seq data. BMC Bioinformatics, 22: 524.
 D. Tang, S. Park, H. Zhao (2020) NITUMID: Nonnegative Matrix Factorization-based Immune-TUmor MIcroenvironment Deconvolution. Bioinformatics, 36: 1344-1350.
 Y. Liu, J. Warren, H. Zhao (2019) A hierarchical Bayesian model for single-cell clustering using RNA-sequencing data. Annals of Applied Statistics, 13: 1733-1752.
© 2022 Hongyu Zhao, Ph.D.
Created by Eddie, Chen and Wangjie.