null Qihuang Zhang, PhD

Associate Investigator, RI-MUHC

Brain Repair and Integrative Neuroscience (BRaIN) Program

Centre for Outcomes Research and Evaluation



biostatistics • single-cell RNA-seq • multi-omics • spatial transcriptomics • metabolomics • measurement error

Research Focus

My research focuses on developing statistical methods and machine-learning algorithms to enrich the toolbox for knowledge discovery in omics data. Particularly, I focus on the multimodal integration of genomic data and tackle challenges related to high dimensionality, measurement errors, misclassification, and missing data. Lately, I have been delving into developing methods for integrating histology data and spatial transcriptomics data to understand the mechanisms of disease progression in neurological disorders. As a biostatistician, I have extensive experience in constructing statistical models for inference and prediction in genetic association studies with longitudinal response variables and complex association structures.

Selected Publications

Click on Pubmed to see my current publications list

  • Zhang Q, Jiang S, Schroeder A, Hu J, Li K, Zhang B, Dai D, Lee EB, Xiao R, Li M. Leveraging spatial transcriptomics data to recover cell locations in single-cell RNA-seq with CeLEry. Nat Commun. 2023 Jul 8;14(1):4050. doi: 10.1038/s41467-023-39895-3. PMID: 37422469.

  • Fan J, Lyu Y, Zhang Q, Wang X, Li M, Xiao R. MuSiC2: cell-type deconvolution for multi-condition bulk RNA-seq data. Brief Bioinform. 2022 Nov 19;23(6):bbac430. doi: 10.1093/bib/bbac430. PMID: 36208175.

  • Zhang Q, Yi GY. Zero-inflated Poisson models with measurement error in the response. Biometrics. 2023 Jun;79(2):1089-1102. doi: 10.1111/biom.13657. Epub 2022 Apr 20. PMID: 35261029.

  • Zhang Q, Yi GY. Generalized network structured models with mixed responses subject to measurement error and misclassification. Biometrics. 2023 Jun;79(2):1073-1088. doi: 10.1111/biom.13623. Epub 2022 Mar 25. PMID: 35032335.

  • Zhang Q, Yi GY, Chen LP, He W. Sentiment analysis and causal learning of COVID-19 tweets prior to the rollout of vaccines. PLoS One. 2023 Feb 24;18(2):e0277878. doi: 10.1371/journal.pone.0277878. PMID: 36827382.