Dongyuan is a Ph.D. student in Bioinformatics. He obtained his B.S. in Biological Sciences, Fudan University and M.S. in Computational Biology, Harvard University. Dongyuan is interested in developing statistical methods in single-cell genomics.
Dongyuan is on the job market this year. Homepage
Currently Tenure-track Assistant Professor in the Department of Genetics and Genome Sciences at UConn Health.
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.
Wang, Q., Zhai, Z., Lian, Q., Song, D., and Li, J.J. (2023). Categorization and analysis of 14 computational methods for estimating cell potency from single-cell RNA-seq data. arXiv.
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 ]
Song, D.*, Chen, S.*, Lee, C.*, Li, K., Ge, X., and Li, J.J. (2023). Synthetic control removes spurious discoveries from double dipping in single-cell and spatial transcriptomics data analyses. bioRxiv. [ SOFTWARE ]
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 ]
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 ]
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 ]
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 ]
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 ]
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 ]
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 ]
46. Song, D. and 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 22:124. [ UCLA NEWS ] [ SOFTWARE ] [ CODE ]
