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Bayesian Joint Modeling of Single-Cell Expression Data and Bulk Spatial Transcriptomic Data

Author

Listed:
  • Jinge Yu

    (Renmin University of China)

  • Qiuyu Wu

    (Renmin University of China)

  • Xiangyu Luo

    (Renmin University of China)

Abstract

Single-cell RNA-sequencing (scRNA-seq) enables gene expression profiling at single-cell resolution, but it loses the spatial information of cells for solid tissues during the tissue dissociation step before sequencing. In contrast, bulk spatial transcriptomics (ST) methods can measure the expression of spatially organized spots in solid tissues, but as a spot comprises dozens of cells, ST expression levels are averaged signals and lack cellular resolution. Joint analysis of these two complementary data types provides the opportunity to recover the spatial patterns of cell types and obtain the cellular enrichment of spots. However, there is a lack of unified statistical methods to achieve this goal. This study develops a Bayesian statistical method named BEATS to jointly model scRNA-seq data and bulk ST data from a common sample in the presence of cellular and spatial heterogeneity. BEATS can simultaneously (a) discover cell types, where cells in a cell type share mean expression profiles; (b) identify spot regions, where a region is a set of spots with the same cellular compositions; and (c) estimate cell-type proportions for each spot region. The Bayesian posterior inference is performed through a hybrid Markov chain Monte Carlo sampling algorithm. Extensive simulation studies and application to datasets on pancreatic ductal adenocarcinoma tissues demonstrate the practical utility of BEATS.

Suggested Citation

  • Jinge Yu & Qiuyu Wu & Xiangyu Luo, 2023. "Bayesian Joint Modeling of Single-Cell Expression Data and Bulk Spatial Transcriptomic Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(3), pages 719-733, December.
  • Handle: RePEc:spr:stabio:v:15:y:2023:i:3:d:10.1007_s12561-021-09308-4
    DOI: 10.1007/s12561-021-09308-4
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    References listed on IDEAS

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    1. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
    2. Fangda Song & Ga Ming Angus Chan & Yingying Wei, 2020. "Flexible experimental designs for valid single-cell RNA-sequencing experiments allowing batch effects correction," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
    3. Beate Vieth & Swati Parekh & Christoph Ziegenhain & Wolfgang Enard & Ines Hellmann, 2019. "A systematic evaluation of single cell RNA-seq analysis pipelines," Nature Communications, Nature, vol. 10(1), pages 1-11, December.
    4. 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.
    5. Xuran Wang & Jihwan Park & Katalin Susztak & Nancy R. Zhang & Mingyao Li, 2019. "Bulk tissue cell type deconvolution with multi-subject single-cell expression reference," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    6. Wang Miao & Peng Ding & Zhi Geng, 2016. "Identifiability of Normal and Normal Mixture Models with Nonignorable Missing Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(516), pages 1673-1683, October.
    7. van Dyk, David A. & Park, Taeyoung, 2008. "Partially Collapsed Gibbs Samplers: Theory and Methods," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 790-796, June.
    Full references (including those not matched with items on IDEAS)

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