AtML: Accurate identification of Arabidopsis thaliana root cell identity via single-cell sequencing analysis using interpretable machine learning

Abstract Single-cell transcriptomics technologies are essential for understanding the developmental trajectory of plant roots. Although methods for interpreting single-cell transcriptomics data are rapidly advancing in Arabidopsis, precisely annotating cell identity and the occasional lack of canonical markers for certain cell types are major challenges in plant single-cell RNA sequencing (scRNA-seq) analysis. In this work, we trained a machine learning system, namely, AtML, using sequencing datasets from six cell subpopulations (comprising a total of 6000 cells) to predict Arabidopsis root cell stages and mine biomarkers via complete model interpretability. The results of performance testing using an external dataset revealed that AtML achieved 96.50% accuracy and 96.51% recall. With the power of interpretability provided by AtML, our model recognized 160 important marker genes, thus contributing to the understanding of cell type annotations. In conclusion, we trained AtML to efficiently identify Arabidopsis root cell stages, thereby providing insights into cellular heterogeneity in Arabidopsis root development studies.