Li, J. J., & Biggin, M. D. (2015). Gene expression. Statistics requantitates the central dogma. Science (New York, N.Y.), 347(6226), 1066–1067.
Ye, Y., & Li, J. J. (2016). NMFP: a non-negative matrix factorization based preselection method to increase accuracy of identifying mRNA isoforms from RNA-seq data. BMC genomics, 17 Suppl 1(Suppl 1), 11.
Gao, R., & Li, J. J. (2017). Correspondence of D. melanogaster and C. elegans developmental stages revealed by alternative splicing characteristics of conserved exons. BMC genomics, 18(1), 234. https://doi.org/10.1186/s12864-017-3600-2
Li, W. V., & Li, J. J. (2018). Modeling and analysis of RNA-seq data: a review from a statistical perspective. Quantitative biology (Beijing, China), 6(3), 195–209.
Li, W. V., & Li, J. J. (2018). An accurate and robust imputation method scImpute for single-cell RNA-seq data. Nature communications, 9(1), 997.
Li, W. V., & Li, J. J. (2019). A statistical simulator scDesign for rational scRNA-seq experimental design. Bioinformatics (Oxford, England), 35(14), i41–i50.
Li, J. J., & Tong, X. (2020). Statistical Hypothesis Testing versus Machine Learning Binary Classification: Distinctions and Guidelines. Patterns (New York, N.Y.), 1(7), 100115.
Song, D., & Li, J. J. (2021). PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data. Genome biology, 22(1), 124.
Li J. J. (2021). A new bioinformatics tool to recover missing gene expression in single-cell RNA sequencing data. Journal of molecular cell biology, 13(1), 1–2.
Xi, N. M., & Li, J. J. (2021). Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data. Cell systems, 12(2), 176–194.e6.