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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. 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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