4.8 Article

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

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NATURE COMMUNICATIONS
卷 13, 期 1, 页码 -

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NATURE PORTFOLIO
DOI: 10.1038/s41467-022-33045-x

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资金

  1. NIH/NIGMS [1RM1 GM131981-02]
  2. American Heart Association-Established Investigator Award
  3. Hoffmann/Schroepfer Foundation
  4. Additional Venture Foundation
  5. Joan and Sanford I. Weill Scholar Fund
  6. NIH/NLM [DP1LM012179]
  7. NIH K08 Mentored Clinical Scientist Research Career Development Award (NHLBI) [1K08HL15378501]
  8. NIH/NHLBI [F30Hl149152]
  9. Dorothy Dee and Marjorie Boring Trust
  10. Dorothy Dee and Marjorie Boring Trust Award
  11. Stanford Medical Scientist Training Program

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devCellPy is a highly accurate and precise machine learning-enabled tool for automated prediction of cell types across complex cellular hierarchies. It has been demonstrated to be useful in constructing a murine cardiac developmental atlas and predicting cardiomyocyte subtypes in human induced pluripotent stem cells.
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.

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