Author

Woochan Lee

Seoul National UniversityORCID 0000-0002-1092-7331Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:scRNA-seq· 3 Assay:ATAC-seq· 1 Research Focus:batch correction / integration· 1 Approach:Bayesian inference· 1 Research Focus:cell type annotation· 1 Approach:deep learning· 1 Research Focus:differential expression· 1

Frequent Co-Authors

Ana-Maria CujbaiD· 2 shared Krzysztof PolańskiiD· 2 shared Sarah Amalia TeichmanniD· 2 shared Alexander S. SteemersiD· 1 shared Amanda J. OliveriD· 1 shared Anna Wilbrey-ClarkiD· 1 shared Antonio M. A. MirandaiD· 1 shared Bianca DumitrascuiD· 1 shared

Activity by Year

2022-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersCell Biology

Ana-Maria CujbaORCID

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3 papers

Recent·Most cited

Multimodal Hypersampling of the adult human heart resolves tissue-specific cell states and disease-associated variants

James J. Cranley, Lukáš Mach, Takahiro Jimba, Kazumasa Kanemaru, Jing Zhao, Shani Perera, Yasemin-Xiomara Zurke, Simon Koplev, Sam N. Barnett, Lu Wang, Kenny Roberts, Rakeshlal Kapuge, Laura Richardson, Krzysztof Polański, Claudia I. Semprich, , Sarah Amalia Teichmann

This paper creates a detailed heart atlas using multimodal data to identify cell states and disease-associated genetic variants in specific heart regions.

bioRxiv · Jun 17, 2026 · PreprintCell Biology

Gene-level alignment of single cell trajectories

Dinithi Sumanaweera, Chenqu Suo, Ana-Maria Cujba, Daniele Muraro, Emma Dann, Krzysztof Polański, Alexander S. Steemers, Woochan Lee, Amanda J. Oliver, Jong-Eun Park, Kerstin B. Meyer, Bianca Dumitrascu, Sarah Amalia Teichmann

Genes2Genes aligns single-cell trajectories using a Bayesian DP framework, revealing gene-level mismatches and highlighting differences in T cell differentiation between in vitro and in vivo states.

Nature Methods · Sep 19, 2024Peer-reviewedBioinformatics

Expression Based Species Deconvolution and Realignment Removes Misalignent Error in Multi-species Single Cell Data

Jaeyong Choi, Woochan Lee, Jung-Ki Yoon, Sun Mi Choi, Chang‐Hoon Lee, Hyeong‐Gon Moon, Sukki Cho, Jin-Haeng Chung, Han‐Kwang Yang, Jong‐Il Kim

The paper develops a bioinformatics pipeline to reduce misalignment errors in mixed human and mouse single-cell RNA sequencing data, improving clustering and accuracy compared to existing methods.

BMC Bioinformatics · May 2, 2022Peer-reviewedBioinformatics