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Dongyuan Song, PhDAssistant Professor in Computational GenomicsGenetics and Genome Sciences
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| Degree | Institution | Major |
|---|---|---|
| BS | Fudan University | Biological Sciences |
| MS | Harvard T.H. Chan School of Public Health | Computational Biology |
| PhD | University of California, Los Angeles | Bioinformatics |
Awards
| Name of Award/Honor | Awarding Organization |
|---|---|
| Dissertation Year Fellowship | UCLA |
| JXTX and CSHL Biology of Genomes Scholarship | The James P. Taylor Foundation for Open Science |
| Summer Mentored Research Fellowship | UCLA |
| Outstanding Undergraduate Student | Fudan University |
The DS Lab (Dongyuan Song or Data Science) studies various data science problems in genomics. Our research focuses on developing novel computational tools for analyzing high-throughput “omics” data, especially for single-cell and spatial omics. By integrating statistical modeling, artificial intelligence, and bioinformatics, the lab aims to provide a more rigorous interpretation of cellular variation from different biological systems. Some specific topics include:
1. AI models and applications in bioinformatics + omics data;
2. Computational modeling of gene co-expression in single-cell and spatial transcriptomics;
3. Detection of differential expression in single-cell and spatial transcriptomics;
4. Integration and joint modeling of multi-sample single-cell and spatial transcriptomics data.
We are looking for students that are interested in developing novel computational and AI methods for analysing omics data.
Students in Ph.D. in Biomedical Science at UConn Health are very welcome to rotate in our lab. If you are interested in computational genomics, please email me (dosong@uchc.edu) or stop by my office (R1151).
Accepting Lab Rotation Students: Fall Block 2026, Spring 1 and 2 Block 2027
Journal Articles
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Flexible and scalable inference of spatially varying correlation in spatial transcriptomics with spCorr.
Genome research 2026 Jun;
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Evaluating the learnability of single-cell large language models on multiple tasks.
BMC genomics 2026 Jun;
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PseudotimeDE-fast: fast testing of differential gene expression along cell pseudotime.
Bioinformatics (Oxford, England) 2025 Nov;41(11):
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ClusterDE: A Statistical Software Package for Removing Double-Dipping Bias in Post-Clustering Differential Expression Analysis.
Journal of computational biology : a journal of computational molecular cell biology 2025 Oct;
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Systematic benchmarking of computational methods to identify spatially variable genes.
Genome biology 2025 Sep;26(1):285
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Worm Perturb-Seq: massively parallel whole-animal RNAi and RNA-seq.
Nature communications 2025 May;16(1):4785
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Decoding heterogeneous single-cell perturbation responses.
Nature cell biology 2025 Feb;
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DNA binding analysis of rare variants in homeodomains reveals homeodomain specificity-determining residues.
Nature communications 2024 Apr;15(1):3110
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scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics.
Nature biotechnology 2024 Feb;42(2):247-252
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scReadSim: a single-cell RNA-seq and ATAC-seq read simulator.
Nature communications 2023 Nov;14(1):7482
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Single-cell generalized trend model (scGTM): a flexible and interpretable model of gene expression trend along cell pseudotime.
Bioinformatics (Oxford, England) 2022 Aug;38(16):3927-3934
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scSampler: fast diversity-preserving subsampling of large-scale single-cell transcriptomic data.
Bioinformatics (Oxford, England) 2022 May;38(11):3126-3127
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Simulating Single-Cell Gene Expression Count Data with Preserved Gene Correlations by scDesign2.
Journal of computational biology : a journal of computational molecular cell biology 2022 Jan;29(1):23-26
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Clipper: p-value-free FDR control on high-throughput data from two conditions.
Genome biology 2021 Oct;22(1):288
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scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling.
Bioinformatics (Oxford, England) 2021 Jul;37(Suppl_1):i358-i366
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scDesign2: a transparent simulator that generates high-fidelity single-cell gene expression count data with gene correlations captured.
Genome biology 2021 May;22(1):163
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PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data.
Genome biology 2021 Apr;22(1):124
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Explaining the ocean's richest biodiversity hotspot and global patterns of fish diversity.
Proceedings. Biological sciences 2018 Oct;285(1888):
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Increased novel single nucleotide polymorphisms in weedy rice populations associated with the change of farming styles: Implications in adaptive mutation and evolution. , 2017
Journal of Systematics and Evolution 2017 Mar;55(2):149–157
Reviews
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Statistics or biology: the zero-inflation controversy about scRNA-seq data.
Genome biology 2022 Jan;23(1):31
| Title or Abstract | Type | Sponsor/Event | Date/Year | Location |
|---|---|---|---|---|
| spCorr: flexible and scalable inference of spatially varying correlation in spatial transcriptomics | Poster | Biology of Genomes Meeting, Cold Spring Harbor Laboratory (CSHL) | 2025 | New York |
| Synthetic control removes spurious discoveries from double dipping in single-cell and spatial transcriptomics data analyses | Talk | International Chinese Statistical Association (ICSA 2025) | 2025 | Storrs |
| Synthetic control removes spurious discoveries from double dipping in single-cell and spatial transcriptomics data analyses | Conference on Statistics in Genomics and Genetics (STATGEN 2025) | 2025 | Minneapolis | |
| Synthetic control removes spurious discoveries from double dipping in single-cell and spatial transcriptomics data analyses | Talk | UMass Med School/UMass Research Methods Meeting | 2025 | online |