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

<p>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&nbsp;<em>devCellPy</em>&nbsp;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&nbsp;<em>devCellPy</em>, we construct a murine cardiac developmental atlas from published datasets encompassing 104,199 cells from E6.5-E16.5 and train&nbsp;<em>devCellPy</em>&nbsp;to generate a cardiac prediction algorithm. Using this algorithm, we observe a high prediction accuracy (&gt;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<em>-</em>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.</p>