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• W4294884148 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." @default.
• W4294884148 created "2022-09-07" @default.
• W4294884148 creator A5009414387 @default.
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• W4294884148 date "2022-09-07" @default.
• W4294884148 modified "2023-10-10" @default.
• W4294884148 title "devCellPy is a machine learning-enabled pipeline for automated annotation of complex multilayered single-cell transcriptomic data" @default.
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• W4294884148 doi "https://doi.org/10.1038/s41467-022-33045-x" @default.
• W4294884148 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36071107" @default.
• W4294884148 hasPublicationYear "2022" @default.
• W4294884148 type Work @default.

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