94. Higgins, C., Li, J.J., Carey, M. (2025). Spatial transcriptomics iterative hierarchical clustering (stIHC): a novel method for identifying spatial gene co-expression modules. Quantitative Biology 13(4):e70011. [ SOFTWARE ] ]

93. Zhou, H.J., Ge, X., and Li, J.J. (2025). ClipperQTL: ultrafast and powerful eGene identification method. Genome Biology 26:207. [ SOFTWARE ]

92. Zhang, H., Li, X., Song, D., Yukselen, O., Nanda, S., Kucukural, A., Li, J.J., Garber, M., and Walhout, A.J.M. (2025). Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq. Nature Communications 16:4785.

90. Liu, P. and Li, J.J. (2025). mcRigor: A statistical method to enhance the rigor of metacell partitioning in single-cell RNA-seq and ATAC-seq data analysis. Lecture Notes in Computer Science 15647:381-385; Sankararaman, S., ed.; Springer, Cham. (RECOMB 2025; proceeding)

89. Wang, C., Ge, X., Song, D., and Li, J.J. (2025). Comment on Data Fission: Splitting a Single Data Point”-Data Fission for Unsupervised Learning: A Discussion on Post-Clustering Inference and the Challenges of Debiasing. Journal of the American Statistical Association 120(549):174-175.”

88. Fang, L., …, Li, J.J., Palmer, A., Frantz, L., Zhou, H., Zhang, Z., and Liu, G.E. (2025). The farm animal genotype-tissue expression (FarmGTEx) project. Nature Genetics 57:786-796.

87. Song, B., Liu, D., Dai, W., McMyn, N.F., Wang, Q., Yang, D., Krejci, A., Vasilyev, A., Untermoser, N., Loregger, A., Song, D., Williams, B., Rosen, B., Cheng, X., Chao, L., Kale, H.T., Zhang, H., Diao, Y., Bürckstümmer, T., Siliciano, J.D., Li, J.J., Siliciano, R.F., Huangfu, D., & Li, W. (2025). Decoding heterogeneous single-cell perturbation responses. Nature Cell Biology 27:493-504.

86. Sankaran, K., Kodikara, S., Li, J.J., and Le Cao, K.A. (2025). Semisynthetic simulation for microbiome data analysis. Briefings in Bioinformatics 26(1):bbaf051.

84. Sun, T., Yuan, J., Zhu, Y., Li, J., Yang, S., Zhou, J., Ge, X., Qu, S., Li, W., Li, J.J., and Li, Y. (2024). Systematic evaluation of methylation-based cell type deconvolution methods for plasma cell-free DNA. Genome Biology 25:318. | [ PDF ]

83. Fernandez, E.G., Mai, W.X., Song, K., Bayley, N.A., Kim, J., Zhu, H., Pioso, M., Young, P., Andrasz, C., Cadet, D., Liau, L.M., Li, G., Yong, W.H., Rodriguez, F., Dixon, S.J., Souers, A.J., Li, J.J., Graeber, T.G., Cloughesy, T.F. & Nathanson, D.A. (2024). Integrated molecular and functional characterization of the intrinsic apoptotic machinery identifies therapeutic vulnerabilities in malignant glioma. Nature Communications 15:10089.

82. Ge, X.*, Li, Y.*, Li, W., and Li, J.J. (2024). Response to “Neglecting normalization impact in semi-synthetic RNA-seq data simulation generates artificial false positives” and “Winsorization greatly reduces false positives by popular differential expression methods when analyzing human population samples”. Genome Biology 25:283.

81. Li, J.J. (2024). Leadership at the Intersection of Statistics & Genomics: A COPSS-NISS Leadership Webinar with Drs. Rafael Irizarry and Mingyao Li. Statistics in Biosciences 16:547-555.

80. Patowary, A., Zhang, P., Jops, C., Vuong, C.K., Ge, X., Hou, K., Kim, M., Gong, N., Margolis, M., Vo, D., Wang, X., Liu, C., Pasaniuc, B., Li, J.J., Gandal, M.J., and De La Torre-Ubieta, L. (2024). Developmental isoform diversity in the human neocortex informs neuropsychiatric risk mechanisms. Science 384(6698):eadh7688.

79. Wang, W.*, Cen, Y.*, Lu, Z.*, Xu, Y., Sun, T., Xiao, Y., Liu, W., Li, J.J., and Wang, C. (2024). scCDC: a computational method for gene-specific contamination detection and correction in single-cell and single-nucleus RNA-seq data. Genome Biology 25:136. [ SOFTWARE ]

78. Chen, Y.E.*, Ge, X.*, Woyshner, K.*, McDermott, M.*, Manousopoulou, A., Ficarro, S., Marto, J., Kexin Li, Wang, L.D., and Li, J.J. (2024). APIR: a universal FDR-control framework for boosting peptide identification power by aggregating multiple proteomics database search algorithms. Genomics, Proteomics & Bioinformatics 22(2):qzae042. [ SOFTWARE ] [ CODE ]

77. Li, J.J., Zhou, H.J., Tong, X., and Bickel, P.J. (2024). Dissecting gene expression heterogeneity: generalized Pearson correlation squares and the K-lines clustering algorithm. Journal of American Statistical Association 119(548):2450-2463. [ SOFTWARE ] | [ PDF ]

76. Cui, Y., Ye, W., Li, J.S., Li, J.J., Vilain, E., Sallam, T., and Li, W. (2024). A genome-wide spectrum of tandem repeat expansions in 338,963 humans. Cell 187(9):2336-2341. | [ PDF ][/vc_column_text]

75. Xia, L.*, Lee, C.*, and Li, J.J. (2024). Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters. Nature Communications 15:1753. (Featured in Nature Communications Editors’ Highlights) [ Nature Methods: Seeing data as t-SNE and UMAP do ] [ SOFTWARE ]

74. Wang, L., Wang, Y.X.R., Li, J.J., and Tong, X. (2024). Hierarchical Neyman-Pearson classification for prioritizing severe disease categories in COVID-19 patient data. Journal of American Statistical Association 119:39-51.

72. Zhang, C., Zhang, S., and Li, J.J. (2023). A Python package itca for information-theoretic classification accuracy: a criterion that guides data-driven combination of ambiguous outcome labels in multiclass classification. Journal of Computational Biology 30(11):1246-1249. (RECOMB 2023; software article; see Publication 65 for the method article) [ SOFTWARE ]

71. Yan, G., Song, D., and Li, J.J. (2023). scReadSim: a single-cell RNA-seq and ATAC-seq read simulator. Nature Communications 14:7482. [ SOFTWARE ] [ PDF ]

70. Xi, N.M. and Li, J.J. (2023). Exploring the optimization of autoencoder design for imputing single-cell RNA sequencing data. Computational and Structural Biotechnology Journal 21:4079-4095.

69. Li, J.J. (2023). How the Monty Hall problem is similar to the false discovery rate in high-throughput data analysis. Nature Biotechnology 41:754-755. | [ PDF ]

68. Yang, L., Chen, X., Lee, C., Shi, J., Lawrence, E.B., Zhang, L., Li, Y., Gao, N., Jung, S.Y., Creighton, C.J., Li, J.J., Cui, Y., Arimura, S., Lei, Y., Li, W., Shen, L. (2023). Functional characterization of age-dependent p16 epimutation reveals biological drivers and therapeutic targets for colorectal cancer. Journal of Experimental & Clinical Cancer Research 42:113.

67. Wu, Y., Jin, M., Fernandez, M., Hart, K.L., Liao, A., Ge, X., Fernandes, S.M., McDonald, T., Chen, Z., Röth, D., Ghoda, L.Y., Marcucci, G., Kalkum, M., Pillai, R.K., Danilov, A.V., Li, J.J., Chen, J., Brown, J.R., Rosen, S.T., Siddiqi, T., Wang, L. (2023). METTL3-mediated m6A modification controls splicing factor abundance and contributes to aggressive CLL. Blood Cancer Discovery 4(3):228-245.

66. Zong, W., Rahman, T., Zhu, L., Zeng, X., Zhang, Y., Zou, J., Liu, S., Ren, Z., Li, J.J., Sibille, E., Lee, A.V., Oesterreich, S., Ma, T., Tseng, G.C. (2023). Transcriptomic congruence analysis for evaluating model organisms. Proc Natl Acad Sci. USA 120(6):e2202584120.

63. Say, I., Chen, Y.E., Sun, M.Z., Li, J.J., and Lu, D.C. (2022). Machine learning predicts improvement of functional outcomes in traumatic brain injury patients after inpatient rehabilitation. Frontiers in Rehabilitation Sciences 3:1005168.

62. Cui, E.H.*, Song, D.*, Wong, W.K., and Li, J.J. (2022). Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime. Bioinformatics 38(16):3927-3934. [ SOFTWARE ] [ CODE ]

61. Song, D.*, Xi, N.M.*, Li, J.J., and Wang, L. (2022). scSampler: fast diversity-preserving subsampling of large-scale single-cell transcriptomic data. Bioinformatics 38(11):3126-3127. [ PYTHON PACKAGE ] [ R PACKAGE ]

60. Eisen, T.J., Li, J.J., and Bartel, D.P. (2022). The interplay between translational efficiency, poly(A) tails, microRNAs, and neuronal activation. RNA 28:808-831.

57. Sun, T., Song, D., Li, W.V., and Li, J.J. (2022). Simulating single-cell gene expression count data with preserved gene correlations by scDesign2. Journal of Computational Biology 29(1):23-26. (RECOMB 2021; software article; see Publication 50 for the method article) [ SOFTWARE ]

55. Shi, J., Xu, J., Chen, Y.E., Li, J.S., Cui, Y., Shen, L, Li, J.J., and Li, W. (2021). The concurrence of DNA methylation and demethylation is associated with transcription regulation. Nature Communications 12:5285.

54. Xi, N.M. and Li, J.J. (2021). Protocol for executing and benchmarking eight computational doublet-detection methods in single-cell RNA sequencing data analysis. STAR Protocols 2(3):100699. [ SOFTWARE ]

51. Wang, N., Lefaudeux, D., Mazumder, A., Li, J.J., Hoffmann, A. (2021). Identifying the combinatorial control of signal-dependent transcription factors. PLOS Computational Biology 17(6):e1009095.

47. Sun, M.Z., Babayan, D., Chen, J.-S., Wang, M.M., Naik, P.K., Reitz, K., Li, J.J., Pouratian, N., Kim, W. (2021). Postoperative admission of adult craniotomy patients to the neuroscience ward reduces length of stay and cost. Neurosurgery 89(1):85-93.

44. Guo, Y., Xue, Z., Yuan, R., Li, J.J., Pastor, W.A., and Liu, W. (2021). RAD: a web application to identify region associated differentially expressed genes. Bioinformatics 37(17):2741-2743. [ WEBSITE ]

43. Xu, J., Shi, J., Cui, X., Cui, Y., Li, J.J., Goel, A., Chen, X., Issa, J.-P., Su, J., and Li, W. (2021). Cellular Heterogeneity-Adjusted cLonal Methylation (CHALM) improves prediction of gene expression. Nature Communcations 12:400.

41. 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. (Highlight of the PBLR method by Zhang and Zhang)

39. Yu, C., Zhang, M., Song, J., Zheng, X., Xu, G., Bao, Y., Lan, J., Luo, D., Hu, J., Li, J.J., and Shi, H. (2020). Integrin-Src-YAP1 signaling mediates the melanoma acquired resistance to MAPK and PI3K/mTOR dual targeted therapy. Molecular Biomedicine 1:12.

31. Li, J.J. (2019). Review of “Statistical modeling and machine learning for molecular biology” by Moses, A.M. The American Statistician 73(1):103-104.

28. Burke, J.E., Longhurst, A.D., Merkurjev, D., Sales-Lee, J., Rao, B., Moresco, J.J., Yates III, J.R., Li, J.J., and Madhani, H.D. (2018). Spliceosome profiling visualizes operations of a dynamic RNP at nucleotide resolution. Cell 173(4):1014-1030.e17.

24. Zhang, Y., Harris, C.J., Liu, Q., Liu, W., Ausin, I., Long, Y., Xiao, L., Feng, L., Chen, X., Xie, Y., Chen, X., Zhan, L., Feng, S., Li, J.J., Wang, H., Zhai, J., and Jacobsen. S.E. (2018). Large-scale comparative epigenomics reveals hierarchical regulation of non-CG methylation in Arabidopsis. Proc Natl Acad Sci. USA 115(5):E1069-E1074.

23. Jonassaint, C.R., Kang, C., Abrams, D.M., Li, J.J., Mao, J., Jia, Y., Long, Q., Sanger, M., Jonassaint, J.C., De Castro, L., and Shah, N. (2018). Understanding patterns and correlates of daily pain using the sickle cell disease mobile application to record symptoms via technology (SMART). British Journal of Haematology 183(2):306-308.

21. Clifton, S.M., Kang, C., Li, J.J., Long, Q., Shah, N., and Abrams, D.M. (2017). Hybrid statistical and mechanistic mathematical model guides mobile health intervention for chronic pain. Journal of Computational Biology 24(7):675-688.

20. Tong, X. and Li, J.J. (2017). Discussion of “Random-projection ensemble classification” by Cannings, T.I. and Samworth, R.J. Journal of the Royal Statistical Society: Series B 79(4):1025-1026.

16. Li, J.J. and Tong, X. (2016). Genomic applications of the Neyman-Pearson classification paradigm. Big Data Analytics in Genomics. Springer (New York).

13. Li, J.J., Huang, H., Qian, M., and Zhang, X. (2015). Chapter 24: Transcriptome analysis using next-generation sequencing. Advanced Medical Statistics (2nd Edition).

12. Liu, Z., Dai, S., Bones, J., Ray, S., Cha, S., Karger, B. L., Li, J.J., Wilson, L., Hinckle, G., and Rossomando, A. (2015). A quantitative proteomic analysis of cellular responses to high glucose media in Chinese hamster ovary cells. Biotechnology Progress 31(4):1026-38.

9. Boyle, A., Araya, C., Brdlik, C., Cayting, P., Cheng, C., Cheng, Y., Gardner, K., Hillier, L., Janette, J., Jiang, L., Kasper, D., Kawli, T., Kheradpour, P., Kundaje, A., Li, J.J., and 25 other authors from the modENCODE and ENCODE consortia (2014). Comparative analysis of regulatory information and circuits across distant species. Nature 512(7515):453-456. [ NIH NEWS ]

6. Fisher, W.W., Li, J.J., Hammonds, A.S., Brown, J.B., Pfeiffer, B., Weiszmann, R., MacArthur, S., Thomas, S., Stamatoyannopoulos, J.A., Eisen, M.B., Bickel, P.B., Biggin, M.D., and Celniker, S.E. (2012). DNA regions bound at low occupancy by transcription factors do not drive patterned reporter gene expression in Drosophila. Proc Natl Acad Sci. USA 109(52):21330-21335.

5. The ENCODE Project Consortium (2012). An integrated encyclopedia of DNA elements in the human genome. Nature 489(7414):57-74. [ UC BERKELEY NEWS ]

4. Gao, Q., Ho, C., Jia, Y., Li, J.J., and Huang, H. (2012). Biclustering of linear patterns in gene expression data (CLiP). Journal of Computational Biology 19(6):619-631.

3. Li, J., Li, J., and Chen, B. (2012). Oct4 was a novel target of Wnt signaling pathway. Molecular and Cellular Biochemistry 362:233-240.