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 the authors present devCellPy, a Python-based package that enables the automated prediction of cell types across complex cellular hierarchies, species, and experimental systems with high accuracy, particularly for developmental scRNA-seq datasets.