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nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes

Author

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  • Lukas M. Weber

    (Johns Hopkins Bloomberg School of Public Health)

  • Arkajyoti Saha

    (University of Washington)

  • Abhirup Datta

    (Johns Hopkins Bloomberg School of Public Health)

  • Kasper D. Hansen

    (Johns Hopkins Bloomberg School of Public Health)

  • Stephanie C. Hicks

    (Johns Hopkins Bloomberg School of Public Health)

Abstract

Feature selection to identify spatially variable genes or other biologically informative genes is a key step during analyses of spatially-resolved transcriptomics data. Here, we propose nnSVG, a scalable approach to identify spatially variable genes based on nearest-neighbor Gaussian processes. Our method (i) identifies genes that vary in expression continuously across the entire tissue or within a priori defined spatial domains, (ii) uses gene-specific estimates of length scale parameters within the Gaussian process models, and (iii) scales linearly with the number of spatial locations. We demonstrate the performance of our method using experimental data from several technological platforms and simulations. A software implementation is available at https://bioconductor.org/packages/nnSVG .

Suggested Citation

  • Lukas M. Weber & Arkajyoti Saha & Abhirup Datta & Kasper D. Hansen & Stephanie C. Hicks, 2023. "nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-39748-z
    DOI: 10.1038/s41467-023-39748-z
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    References listed on IDEAS

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    1. Abhirup Datta & Sudipto Banerjee & Andrew O. Finley & Alan E. Gelfand, 2016. "Hierarchical Nearest-Neighbor Gaussian Process Models for Large Geostatistical Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 800-812, April.
    2. Madhav Mantri & Gaetano J. Scuderi & Roozbeh Abedini-Nassab & Michael F. Z. Wang & David McKellar & Hao Shi & Benjamin Grodner & Jonathan T. Butcher & Iwijn De Vlaminck, 2021. "Spatiotemporal single-cell RNA sequencing of developing chicken hearts identifies interplay between cellular differentiation and morphogenesis," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    3. Chee-Huat Linus Eng & Michael Lawson & Qian Zhu & Ruben Dries & Noushin Koulena & Yodai Takei & Jina Yun & Christopher Cronin & Christoph Karp & Guo-Cheng Yuan & Long Cai, 2019. "Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH+," Nature, Nature, vol. 568(7751), pages 235-239, April.
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    Cited by:

    1. Juexin Wang & Jinpu Li & Skyler T. Kramer & Li Su & Yuzhou Chang & Chunhui Xu & Michael T. Eadon & Krzysztof Kiryluk & Qin Ma & Dong Xu, 2023. "Dimension-agnostic and granularity-based spatially variable gene identification using BSP," Nature Communications, Nature, vol. 14(1), pages 1-15, December.

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