Research

Statistical Genetics

• Leveraging Hardy–Weinberg disequilibrium for association testing in case-control studies

Zhang, L., Strug, L.J., and Sun, L. (2023). Annals of Applied Statistics, 17(2): 1764-1781.

• Unifying genetic association tests via regression: Prospective and retrospective, parametric and non-parametric, and genotype- and allele-based tests

       Zhang, L. and Sun, L. (2022). Canadian Journal of Statistics.

• Linear mixed-effect models through the lens of Hardy-Weinberg disequilibrium

       Zhang, L. and Sun, L. (2022). Frontiers in Genetics, 13,  856872-856872. 

• A Generalized Robust Allele-based Genetic Association Test

Zhang, L. and Sun, L. (2022). Biometrics, 78(2), pp. 487-498.

• BLUE and AIM: best linear unbiased allele-frequency estimation and ancestry informative marker testing via retrospective regression.

Zhang, L., Paterson, A.D., and Sun, L. (in preparation). 

Computational Biology

• Characterizing the impacts of dataset imbalance on single-cell data integration

      Maan, H., Zhang, L., Yu, C., Geuenich, M.J., Campbell, K.R., Wang, B. (2024). Nature Biotechnology, pp.1-10.

• Decentralised, collaborative, and privacy-preserving machine learning for multi-hospital data

      Fang, C., Dziedzic, A., Zhang, L., Oliva, L., Verma, A., Razak, F., Papernot, N. and Wang, B. (2024). eBioMedicine, 101.

• One Cell At a Time: A Unified Framework to Integrate and Analyze Single-cell RNA-seq Data

       Wang, C. X.*, Zhang, L.* and Wang, B. (2022). Genome Biology, 23, 102.

• simATAC: a single-cell ATAC-seq simulation framework

Navidi, Z., Zhang, L. and Wang, B. (2021).  Genome Biology, 22, 74.

• scHash: an interpretable and scalable deep hashing approach for cell annotations of single-cell omics data

Shao, C.*, Zhang, L.*, Ma, S., and Wang, B. (in preparation) 

• Analytics methods and tools for integration of biomedical data in medicine. 

Zhang, L., Karimzadeh, M., Welch, M., McIntosh, C. & Wang, B. (2020). In Artificial intelligence in medicine: technical basis and clinical applications, 1st Edition.  Xing, L,  Giger, M. & Min, J. (Editors). Springer. 

Interdisciplinary Collaborations

• Single-cell profiling of healthy human kidney reveals features of sex-based transcriptional programs and tissue-specific immunity

       McEvoy, C.M., Murphy, J.M., Zhang, L., Clotet-Freixas, S., Mathews, J.A., An, J., Karimzadeh, M., Pouyabahar, D., Su, S., Zaslaver, O., Rost, H.,  Arambewela, M., Liu, L.Y., Zhang, S., Lawson, K.A., Finelli, A., Wang, B., MacParland, S.A., Bader, G.D., Konvalinka, A., and Crome, S.Q. (2022). Nature Communications, 13, 7634.

• Genetic association and transcriptome integration identify contributing genes and tissues at cystic fibrosis modifier loci.

Gong, J., Wang, F., Xiao, B., Panjwani, N., Lin, F., Keenan, K., Avolio, J., Esmaeili, M., Zhang, L., He, G., Soave, D., Mastromatteo, S., Baskurt, Z., Kim, S., O'Neal, W. K., PolineniI, D., Blackman, S.M., Corvol, H., Cutting, G. R., Drumm, M., Knowles, M., Rommens, J. M., Sun, L., and Strug, L.J. (2019). PLoS Genetics, 15(2), p.e1008007. 

• Improving imputation in disease-relevant regions: lessons from cystic fibrosis.

Panjwani, N., Xiao, B., Xu, L., Gong, J., Keenan, K., Lin, F., He, G., Baskurt, Z., Kim, S., Zhang, L., Esmaeili, M., Blackman, S., Scherer, S.W., Corvol, H.,  Drumm, M., Knowles, M., Cutting, G., Rommens, J.M., Sun, L., and Strug, L.J. (2018). NPJ Genomic Medicine, 3(1), pp.1-5, . 

• Learning global and local consistent representations for unsupervised image retrieval via deep graph diffusion networks. 

Dou, Z., Cui, H., Zhang, L., and Wang, B. (2020), arXiv:2001.01284.