Statistics or biology: the zero-inflation controversy about scRNA-seq data
58. Jiang, R., Sun, T., Song, D., and Li, J.J. (2022). Statistics or biology: the zero-inflation controversy about scRNA-seq data. Genome Biology 23:31. [ CODE ] [ PDF ]
Simulating Single-Cell Gene Expression Count Data with Preserved Gene Correlations by scDesign2
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 ]
Clipper: p-value-free FDR control on high-throughput data from two conditions
56. Ge, X.*, Chen, Y.E.*, Song, D., McDermott, M., Woyshner, K., Manousopoulou, A., Wang, N., Li, W., Wang, L.D., and Li, J.J. (2021). Clipper: p-value-free FDR control on high-throughput data from two conditions. Genome Biology 22:288. [ UCLA NEWS ] [ SOFTWARE ] [ CODE ] [ VIDEO ] | [ PDF ]
The concurrence of DNA methylation and demethylation is associated with transcription regulation
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.
Protocol for benchmarking computational doublet-detection methods in single-cell RNA sequencing data analysis
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 ]
scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling.
53. Song, D.*, Li, K.*, Hemminger, Z., Wollman, R., and Li, J.J. (2021). scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling. Bioinformatics 37(Supplement_1):i358-i366. [ ISMB/ECCB 2021 ] [ SOFTWARE ]
mbImpute: an accurate and robust imputation method for microbiome data
52. Jiang, R., Li, W.V., and Li, J.J. (2021). mbImpute: an accurate and robust imputation method for microbiome data. Genome Biology 22:192. [ UCLA NEWS ] [ SOFTWARE ] | [ PDF ]
Identifying the combinatorial control of signal-dependent transcription factors
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.
scDesign2: an interpretable simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured
50. Sun, T., Song, D., Li, W.V., and Li, J.J. (2021). scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured. Genome Biology 22:163. [ RECOMB 2021 ] [ UCLA NEWS ] [ SOFTWARE ] [ CODE ] [ PDF ]
Neyman-Pearson Criterion (NPC): A Model Selection Criterion for Asymmetric Binary Classification
49. Li, J.J., Chen, Y.E., and Tong, X. (2021). A flexible model-free prediction-based framework for feature ranking. Journal of Machine Learning Research 22(124):1-54. [ SOFTWARE ]