Author

Takahiro Jimba

The University of TokyoORCID 0000-0002-3757-3959Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Research Focus:cell type annotation· 2 Approach:deep learning· 2 Organism:human· 2 Assay:scRNA-seq· 2 Assay:ATAC-seq· 1 Research Focus:benchmarking· 1 Research Focus:clustering· 1 Approach:contrastive learning· 1

Frequent Co-Authors

James J. CranleyiD· 2 shared Sarah Amalia TeichmanniD· 2 shared Ana-Maria CujbaiD· 1 shared Anna Wilbrey-ClarkiD· 1 shared Antonio M. A. MirandaiD· 1 shared Batuhan ÇakıriD· 1 shared Bulgantamir Tegshjargal· 1 shared Claudia I. SemprichiD· 1 shared

Activity by Year

2024-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 30 mutual co-author links

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2 shared papersCell Biology

James J. CranleyORCID

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2 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

inVAE: Conditionally invariant representation learning for generating multivariate single-cell reference maps

Hananeh Aliee, Ferdinand Kapl, Duy Pham, Batuhan Çakır, Takahiro Jimba, James J. Cranley, Sarah Amalia Teichmann, Kerstin B. Meyer, Roser Vento-Tormo, Fabian Joachim Theis

inVAE improves cell state identification by disentangling biological and technical variation in single-cell data, outperforming existing methods across multiple human tissues.

bioRxiv · Dec 6, 2024 · PreprintBioinformatics