Dongyuan Song

Dongyuan Song

Assitant Professor in Computational Genomics, UConn Health

About me

Hi, I’m Dongyuan, a new tenure track assistant professor in Department of Genetics and Genome Sciences, University of Connecticut, Health Center (UConn Health).

Previously, I got my Ph.D. in Bioinformatics, at University of California, Los Angeles (UCLA), under the supervision of Dr. Jingyi Jessica Li from Department of Statistics & Data Science. Previously, I received Master of Science in Computational Biology & Quantitative Genetics from Department of Biostatistcs, Harvard T.H. Chan School of Public Health, under supervision of Dr. Rafael Irizarry. I obtained Bachelor of Science in Biological Science from Fudan University, Shanghai, China, under supervision of Dr. Bao-Rong Lu.

I am looking for students and postdocs to work on computational genomics. Please email dosong@uchc.edu if you are interested in. Perspective student should first apply Ph.D. in Biomedical Science. The deadline of this year is 12/01/2024. Current students in Ph.D. in Biomedical Science are welcome for rotation. For more details, please check our group website DS lab.

My research focuses on developing computational tools in analyzing single-cell and spatial omics. Some of my previous works include:

  1. Probabilistic generative models of high-dimensional single-cell and spatial multi-omics data. I develped scDesign3 (Nature Biotechnology, 2024), an “all-in-one” multimodal single-cell and spatial omics simulator which summarizes the input real dataset into a parametric model. I also contributed to the development of scReadSim (Nature Communications, 2023), a single-cell RNA-seq and ATAC-seq read simulator, and scDesign2 (Genome Biology, 2021), the predecessor of scDesign3.
  2. Differential expression (DE) test and false discovery rate (FDR). We developed ClusterDE (bioRxiv, 2023), a post-clustering DE method controlling FDR under “double dipping (i.e., first clustering then DE between clusters)”. Previously, I developed PseudotimeDE (Genome Biology, 2021), a DE method for testing gene changes along cell pseudotime accounting for the uncertainty of pseudotime.
  3. Informative genes/cells selection in large-scale scRNA-seq data. We developed scPNMF (Bioinformatics, 2021), an informative gene selection method for selecting only a small number of genes. We also developed scSampler (Bioinformatics, 2022), a diveristy-preserving cell subsampling method for large-scale datasets.