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

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

SUBMITTER: Galdos FX 

PROVIDER: S-EPMC9452519 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature