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

Francisco X. Galdos, Sidra Xu, William R. Goodyer, Lauren Duan, Yuhsin V. Huang, Soah Lee, Han Zhu, Carissa Lee, Nicholas Wei, Daniel Lee and Sean M. Wu ()
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Francisco X. Galdos: Stanford University School of Medicine
Sidra Xu: Stanford University School of Medicine
William R. Goodyer: 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
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

Nature Communications, 2022, vol. 13, issue 1, 1-20

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-33045-x

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