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Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data

Author

Listed:
  • Brendan F. Miller

    (Johns Hopkins University
    Johns Hopkins University)

  • Feiyang Huang

    (Johns Hopkins University
    Johns Hopkins University)

  • Lyla Atta

    (Johns Hopkins University
    Johns Hopkins University)

  • Arpan Sahoo

    (Johns Hopkins University
    Johns Hopkins University)

  • Jean Fan

    (Johns Hopkins University
    Johns Hopkins University
    Johns Hopkins University)

Abstract

Recent technological advancements have enabled spatially resolved transcriptomic profiling but at multi-cellular pixel resolution, thereby hindering the identification of cell-type-specific spatial patterns and gene expression variation. To address this challenge, we develop STdeconvolve as a reference-free approach to deconvolve underlying cell types comprising such multi-cellular pixel resolution spatial transcriptomics (ST) datasets. Using simulated as well as real ST datasets from diverse spatial transcriptomics technologies comprising a variety of spatial resolutions such as Spatial Transcriptomics, 10X Visium, DBiT-seq, and Slide-seq, we show that STdeconvolve can effectively recover cell-type transcriptional profiles and their proportional representation within pixels without reliance on external single-cell transcriptomics references. STdeconvolve provides comparable performance to existing reference-based methods when suitable single-cell references are available, as well as potentially superior performance when suitable single-cell references are not available. STdeconvolve is available as an open-source R software package with the source code available at https://github.com/JEFworks-Lab/STdeconvolve .

Suggested Citation

  • Brendan F. Miller & Feiyang Huang & Lyla Atta & Arpan Sahoo & Jean Fan, 2022. "Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30033-z
    DOI: 10.1038/s41467-022-30033-z
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    1. Kai Kang & Qian Meng & Igor Shats & David M Umbach & Melissa Li & Yuanyuan Li & Xiaoling Li & Leping Li, 2019. "CDSeq: A novel complete deconvolution method for dissecting heterogeneous samples using gene expression data," PLOS Computational Biology, Public Library of Science, vol. 15(12), pages 1-18, December.
    2. Michael J. Hawrylycz & Ed S. Lein & Angela L. Guillozet-Bongaarts & Elaine H. Shen & Lydia Ng & Jeremy A. Miller & Louie N. van de Lagemaat & Kimberly A. Smith & Amanda Ebbert & Zackery L. Riley & Chr, 2012. "An anatomically comprehensive atlas of the adult human brain transcriptome," Nature, Nature, vol. 489(7416), pages 391-399, September.
    3. 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.
    4. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    5. Mihriban Karaayvaz & Simona Cristea & Shawn M. Gillespie & Anoop P. Patel & Ravindra Mylvaganam & Christina C. Luo & Michelle C. Specht & Bradley E. Bernstein & Franziska Michor & Leif W. Ellisen, 2018. "Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    6. Kushal K Dey & Chiaowen Joyce Hsiao & Matthew Stephens, 2017. "Visualizing the structure of RNA-seq expression data using grade of membership models," PLOS Genetics, Public Library of Science, vol. 13(3), pages 1-23, March.
    7. 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. Haojia Wu & Eryn E. Dixon & Qiao Xuanyuan & Juanru Guo & Yasuhiro Yoshimura & Chitnis Debashish & Anezka Niesnerova & Hao Xu & Morgane Rouault & Benjamin D. Humphreys, 2024. "High resolution spatial profiling of kidney injury and repair using RNA hybridization-based in situ sequencing," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    2. Kalen Clifton & Manjari Anant & Gohta Aihara & Lyla Atta & Osagie K. Aimiuwu & Justus M. Kebschull & Michael I. Miller & Daniel Tward & Jean Fan, 2023. "STalign: Alignment of spatial transcriptomics data using diffeomorphic metric mapping," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    3. Haoyang Li & Juexiao Zhou & Zhongxiao Li & Siyuan Chen & Xingyu Liao & Bin Zhang & Ruochi Zhang & Yu Wang & Shiwei Sun & Xin Gao, 2023. "A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics," Nature Communications, Nature, vol. 14(1), pages 1-10, December.

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