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devCellPy is a machine learning-enabled pipeline for automated annotation of complex multilayered single-cell transcriptomic data

Author

Listed:
  • Francisco X. Galdos

    (Stanford University School of Medicine
    Stanford University School of Medicine)

  • Sidra Xu

    (Stanford University School of Medicine)

  • William R. Goodyer

    (Stanford University School of Medicine
    Stanford University School of Medicine
    Stanford University School of Medicine)

  • Lauren Duan

    (Stanford University School of Medicine)

  • Yuhsin V. Huang

    (Stanford University School of Medicine)

  • Soah Lee

    (Sungkyunkwan University)

  • Han Zhu

    (Stanford University School of Medicine
    Stanford University School of Medicine)

  • Carissa Lee

    (Stanford University School of Medicine)

  • Nicholas Wei

    (Stanford University School of Medicine)

  • Daniel Lee

    (Stanford University School of Medicine)

  • Sean M. Wu

    (Stanford University School of Medicine
    Stanford University School of Medicine
    Stanford University School of Medicine)

Abstract

A major informatic challenge in single cell RNA-sequencing analysis is the precise annotation of datasets where cells exhibit complex multilayered identities or transitory states. Here, we present devCellPy a highly accurate and precise machine learning-enabled tool that enables automated prediction of cell types across complex annotation hierarchies. To demonstrate the power of devCellPy, we construct a murine cardiac developmental atlas from published datasets encompassing 104,199 cells from E6.5-E16.5 and train devCellPy to generate a cardiac prediction algorithm. Using this algorithm, we observe a high prediction accuracy (>90%) across multiple layers of annotation and across de novo murine developmental data. Furthermore, we conduct a cross-species prediction of cardiomyocyte subtypes from in vitro-derived human induced pluripotent stem cells and unexpectedly uncover a predominance of left ventricular (LV) identity that we confirmed by an LV-specific TBX5 lineage tracing system. Together, our results show devCellPy to be a useful tool for automated cell prediction across complex cellular hierarchies, species, and experimental systems.

Suggested Citation

  • Francisco X. Galdos & Sidra Xu & William R. Goodyer & Lauren Duan & Yuhsin V. Huang & Soah Lee & Han Zhu & Carissa Lee & Nicholas Wei & Daniel Lee & Sean M. Wu, 2022. "devCellPy is a machine learning-enabled pipeline for automated annotation of complex multilayered single-cell transcriptomic data," Nature Communications, Nature, vol. 13(1), pages 1-20, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-33045-x
    DOI: 10.1038/s41467-022-33045-x
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    References listed on IDEAS

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    1. Yena Oh & Rimshah Abid & Saif Dababneh & Marwan Bakr & Termeh Aslani & David P. Cook & Barbara C. Vanderhyden & Jin G. Park & Nikhil V. Munshi & Chi-Chung Hui & Kyoung-Han Kim, 2024. "Transcriptional regulation of the postnatal cardiac conduction system heterogeneity," Nature Communications, Nature, vol. 15(1), pages 1-18, December.

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