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Model-based prediction of spatial gene expression via generative linear mapping

Okochi, Yasushi Sakaguchi, Shunta Nakae, Ken Kondo, Takefumi Honda, Naoki 京都大学 DOI:10.1038/s41467-021-24014-x

2021

概要

Decoding spatial transcriptomes from single-cell RNA sequencing (scRNA-seq) data has become a fundamental technique for understanding multicellular systems; however, existing computational methods lack both accuracy and biological interpretability due to their model-free frameworks. Here, we introduce Perler, a model-based method to integrate scRNA-seq data with reference in situ hybridization (ISH) data. To calibrate differences between these datasets, we develop a biologically interpretable model that uses generative linear mapping based on a Gaussian mixture model using the Expectation–Maximization algorithm. Perler accurately predicts the spatial gene expression of Drosophila embryos, zebrafish embryos, mammalian liver, and mouse visual cortex from scRNA-seq data. Furthermore, the reconstructed transcriptomes do not over-fit the ISH data and preserved the timing information of the scRNA-seq data. These results demonstrate the generalizability of Perler for dataset integration, thereby providing a biologically interpretable framework for accurate reconstruction of spatial transcriptomes in any multicellular system.

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Acknowledgements

This study was supported in part by the Cooperative Study Program of Exploratory Research

Center on Life and Living Systems (ExCELLS; program Nos. 18-201, 19-102, and 19-202 to

H.N.); Moonshot R&D–MILLENNIA Program (Grant No.: JPMJMS2024-9) by JST; a Grantin-Aid for Young Scientists (B) (19H04776 and 21H03541 to H.N.), a Grant-in-Aid for

Scientific Research (B) (17KT0021 to T.K.), and a JSPS research fellowship for young scientist

(to S.S.) from the Japan Society for the Promotion of Science (JSPS); the Naito Foundation (to

T.K.); and the Keihanshin Consortium for Fostering the Next Generation of Global Leaders in

NATURE COMMUNICATIONS | (2021)12:3731 | https://doi.org/10.1038/s41467-021-24014-x | www.nature.com/naturecommunications

A Self-archived copy in

Kyoto University Research Information Repository

https://repository.kulib.kyoto-u.ac.jp

NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-021-24014-x

Research (K-CONNEX) established by the program of Building of Consortia for the Development of Human Resources in Science and Technology, MEXT (to T.K.).

Author contributions

H.N., S.S., and T.K. conceived the project. Y.O., H.N., and K.N. developed the method,

Y.O. implemented the software, and Y.O. and S.S. analyzed data. Y.O. and H.N. wrote the

manuscript with input from all authors.

Competing interests

The authors declare no competing interests.

Additional information

Supplementary information The online version contains supplementary material

available at https://doi.org/10.1038/s41467-021-24014-x.

Correspondence and requests for materials should be addressed to H.N.

Peer review information Nature Communications thanks Pablo Meyer, Xianwen Ren

and other, anonymous, reviewers for their contributions to the peer review of this work.

Peer review reports are available.

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© The Author(s) 2021

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