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Spatially aware dimension reduction for spatial transcriptomics

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  • Lulu Shang

    (University of Michigan
    University of Michigan)

  • Xiang Zhou

    (University of Michigan
    University of Michigan)

Abstract

Spatial transcriptomics are a collection of genomic technologies that have enabled transcriptomic profiling on tissues with spatial localization information. Analyzing spatial transcriptomic data is computationally challenging, as the data collected from various spatial transcriptomic technologies are often noisy and display substantial spatial correlation across tissue locations. Here, we develop a spatially-aware dimension reduction method, SpatialPCA, that can extract a low dimensional representation of the spatial transcriptomics data with biological signal and preserved spatial correlation structure, thus unlocking many existing computational tools previously developed in single-cell RNAseq studies for tailored analysis of spatial transcriptomics. We illustrate the benefits of SpatialPCA for spatial domain detection and explores its utility for trajectory inference on the tissue and for high-resolution spatial map construction. In the real data applications, SpatialPCA identifies key molecular and immunological signatures in a detected tumor surrounding microenvironment, including a tertiary lymphoid structure that shapes the gradual transcriptomic transition during tumorigenesis and metastasis. In addition, SpatialPCA detects the past neuronal developmental history that underlies the current transcriptomic landscape across tissue locations in the cortex.

Suggested Citation

  • Lulu Shang & Xiang Zhou, 2022. "Spatially aware dimension reduction for spatial transcriptomics," Nature Communications, Nature, vol. 13(1), pages 1-22, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34879-1
    DOI: 10.1038/s41467-022-34879-1
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    1. 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.
    2. Michael E. Tipping & Christopher M. Bishop, 1999. "Probabilistic Principal Component Analysis," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(3), pages 611-622.
    3. Gökcen Eraslan & Lukas M. Simon & Maria Mircea & Nikola S. Mueller & Fabian J. Theis, 2019. "Single-cell RNA-seq denoising using a deep count autoencoder," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    4. Ed S. Lein & Michael J. Hawrylycz & Nancy Ao & Mikael Ayres & Amy Bensinger & Amy Bernard & Andrew F. Boe & Mark S. Boguski & Kevin S. Brockway & Emi J. Byrnes & Lin Chen & Li Chen & Tsuey-Ming Chen &, 2007. "Genome-wide atlas of gene expression in the adult mouse brain," Nature, Nature, vol. 445(7124), pages 168-176, January.
    5. Davide Risso & Fanny Perraudeau & Svetlana Gribkova & Sandrine Dudoit & Jean-Philippe Vert, 2018. "A general and flexible method for signal extraction from single-cell RNA-seq data," Nature Communications, Nature, vol. 9(1), pages 1-17, December.
    6. 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.
    7. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    8. Alma Andersson & Ludvig Larsson & Linnea Stenbeck & Fredrik Salmén & Anna Ehinger & Sunny Z. Wu & Ghamdan Al-Eryani & Daniel Roden & Alex Swarbrick & Åke Borg & Jonas Frisén & Camilla Engblom & Joakim, 2021. "Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions," Nature Communications, Nature, vol. 12(1), pages 1-14, December.
    9. Suijuan Zhong & Shu Zhang & Xiaoying Fan & Qian Wu & Liying Yan & Ji Dong & Haofeng Zhang & Long Li & Le Sun & Na Pan & Xiaohui Xu & Fuchou Tang & Jun Zhang & Jie Qiao & Xiaoqun Wang, 2018. "A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex," Nature, Nature, vol. 555(7697), pages 524-528, March.
    10. Anjali Rao & Dalia Barkley & Gustavo S. França & Itai Yanai, 2021. "Exploring tissue architecture using spatial transcriptomics," Nature, Nature, vol. 596(7871), pages 211-220, August.
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    2. Zhiyuan Yuan, 2024. "MENDER: fast and scalable tissue structure identification in spatial omics data," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

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