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DevCellPy: A machine learning-enabled pipeline for automated annotation of complex multilayered single-cell transcriptomic data

ABSTRACT: devCellPy: A machine learning-enabled pipeline for automated annotation of complex multilayered single-cell transcriptomic data

PROVIDER: PRJNA767076 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR16101500_1.fastq.gz	Fastqsanger.gz
	SRR16101500_2.fastq.gz	Fastqsanger.gz
	SRR16101600_1.fastq.gz	Fastqsanger.gz
	SRR16101600_2.fastq.gz	Fastqsanger.gz
	SRR16101700_1.fastq.gz	Fastqsanger.gz

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

Project description: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 powerful tool for automated cell prediction across complex cellular hierarchies, species, and experimental systems.

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