Education
- Ph.D. in Bioinformatics, UCLA, 2024
- MS in Computational Biology & Quantitative Genetics, Harvard School of Public Health, 2019
- BS in Biological Science, Fudan University, 2017
Bio
Hi, I am Dongyuan Song (宋东源), a tenure-track Assistant Professor in the Department of Genetics and Genome Sciences at UConn Health. Before joining UConn Health in November 2024, I completed my PhD training in statistical genomics with Jingyi Jessica Li at UCLA. I did my undergraduate research in evolutionary biology with Baorong Lu at Fudan and master’s research in bioinformatics with Rafael Irizarry at Dana-Farber Cancer Institute. My research focuses on developing computational methods for analyzing modern “omics” data, especially for single-cell and spatial omics. In recent years, I have published a series of bioinformatics tools: spCorr (Genome Research, 2026), scDesign3 (Nature Biotechnology, 2024), scReadSim (Nature Communications, 2023), scSampler (Bioinformatics, 2022), scGTM (Bioinformatics, 2022), scDesign2 (Genome Biology, 2021), PseudotimeDE (Genome Biology, 2021), and scPNMF (Bioinformatics, 2021).
Representative Works
All publicationsPeer Reviewed
- Jiang, C.F., Yin, Y., Robson, P., Li, Y.J., Li, J.J. and Song, D. (2026). Flexible and scalable inference of spatially varying correlation in spatial transcriptomics with spCorr. Genome Research.
- Yan, Y., Wang, X. and Song, D. (2026). Evaluating the learnability of single-cell large language models on multiple tasks. BMC Genomics.
- Lai, Y., Song, D., Xia, L. and Li, J.J. (2025). PseudotimeDE-fast: fast testing of differential gene expression along cell pseudotime. Bioinformatics 41(11): btaf573.
- Song, D., Chen, S., Lee, C., Li, K., Ge, X. and Li, J.J. (2025). Synthetic control removes spurious discoveries from double dipping in single-cell and spatial transcriptomics data analyses. Research in Computational Molecular Biology (RECOMB 2025), 400-404.
- Song, D., Wang, Q., Yan, G., Liu, T., Sun, T. and Li, J.J. (2024). scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics. Nature Biotechnology 42(2): 247-252.
- 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.
- 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.
- 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.
- 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(1): 124.
Preprints
- Li, M., Wang, X., Xu, S., Yin, Y., Chen, D., Guo, X. and Song, D. (2026). Trustworthy super-resolution reconstruction across spatial omics modalities. bioRxiv.
- Xu, S., Wang, Y., Meng, L., Dalal, A., Yin, Y. and Song, D. (2026). spAlignDE unifies cross-sample and cross-modal spatial alignment with mismatch-aware differential expression. bioRxiv.