Dongyuan Song

PhD student in Bioinformatics IDP
dongyuansong@ucla.edu

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

Google Scholar

Publications:

72. 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 ]

69. Song, D., Wang, Q., Yan, G., Liu, T., and Li, J.J. (2023). scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics. Nature Biotechnology[ 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 ]