All Published

APIR: a universal FDR-control framework for boosting peptide identification power by aggregating multiple proteomics database search algorithms

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 ]

Dissecting gene expression heterogeneity: generalized Pearson correlation squares and the K-lines clustering algorithm

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 ]

A genome-wide spectrum of tandem repeat expansions in 338,963 humans

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 ]

scDEED: a statistical method for detecting dubious 2D single-cell embeddings

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 ]

scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics

73. Song, D., Wang, Q., Yan, G., Liu, T., and Li, J.J. (2024). scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics. Nature Biotechnology 42:247-252. [ SOFTWARE ] [ PDF ]

Hierarchical Neyman-Pearson classification for prioritizing severe disease categories in COVID-19 patient data

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.

A Python Package itca for Information-Theoretic Classification Accuracy: A Criterion That Guides Data-Driven Combination of Ambiguous Outcome Labels in Multiclass Classification

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 ]