ArticleNature communications2022
devCellPy is a machine learning-enabled pipeline for automated annotation of complex multilayered single-cell transcriptomic data.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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27 citing papers in PubMed.
- Article
- Machine learning-driven decoding of maternal immune signatures in repeated pregnancy loss.PLoS computational biology · 2026Article
- scMapNet: Marker-based cell type annotation of scRNA-seq data via vision transfer learning with tabular-to-image transformations.Journal of advanced research · 2026Article
- Why biology must prioritize data reanalysis in the era of artificial intelligence.PLoS biology · 2026Article
- Hierarchical classification of immune cell transcriptomes at population-scale.bioRxiv : the preprint server for biology · 2026Article
- Divide-and-conquer analysis reveals hidden immune cell influencers across the placenta in preeclampsia.BioData mining · 2026Article
- Decoding heart failure subtypes with neural networks via differential explanation analysis.Briefings in bioinformatics · 2025Article
- ProjectSVR: mapping single-cell RNA-seq data to reference atlases by supported vector regression.Briefings in bioinformatics · 2025Article
- Gene print-based cell subtypes annotation of human disease across heterogeneous datasets with gPRINT.Protein & cell · 2025Article
- Interpretable and integrative analysis of single-cell multiomics with scMKL.Communications biology · 2025Article
- Interpretable machine learning-guided single-cell mapping deciphers multi-lineage pancreatic dysregulation in type 2 diabetes.Cardiovascular diabetology · 2025Article
- scaLR: a low-resource deep neural network-based platform for single cell analysis and biomarker discovery.Briefings in bioinformatics · 2025Article
- Harnessing the power of artificial intelligence for human living organoid research.Bioactive materials · 2024Review
- Review
- Identification of kidney cell types in scRNA-seq and snRNA-seq data using machine learning algorithms.Heliyon · 2024Article
- Systems immunology approaches to study T cells in health and disease.NPJ systems biology and applications · 2024Review
- Transcriptional regulation of the postnatal cardiac conduction system heterogeneity.Nature communications · 2024Article
- Single-Cell Sequencing Technology in Ruminant Livestock: Challenges and Opportunities.Current issues in molecular biology · 2024Review
- Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets.Cell reports. Medicine · 2024Article
- Uncertainty-aware single-cell annotation with a hierarchical reject option.Bioinformatics (Oxford, England) · 2024Article
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11 authors.
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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.
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