Global trends in machine learning applications for single-cell transcriptomics research.

Background: Single-cell RNA sequencing (scRNA-seq) has revolutionized cellular heterogeneity analysis by decoding gene expression profiles at individual cell level, while machine learning (ML) has emerged as core computational tool for clustering analysis, dimensionality reduction modeling and developmental trajectory inference in single-cell transcriptomics(SCT). Although 3,307 papers have been published in past two decades, there remains lack of bibliometric review comprehensively addressing methodological evolution, technical challenges and clinical translation pathways. This study aims to fill research gap through bibliometric and visual analysis, revealing technological evolution trends and future development directions.

Methods: Using 3,307 publications from Web of Science Core Collection(WOSCC), we conducted bibliometric and visualization analysis through CiteSpace and VOSviewer to systematically review research trends, national/institutional contributions, keyword co-occurrence networks and co-citation relationships. Data screening strictly limited to English articles and reviews, excluding irrelevant document types, focusing on core application scenarios of ML in SCT.

Results: China and United States dominated research output (combined 65%), with China leading in publication volume (54.8%) while US demonstrating academic influence through H-index 84 and 37,135 total citations. Research hotspots concentrated on random forest (RF) and deep learning models, showing transition from algorithm development to clinical applications (e.g., tumor immune microenvironment analysis). Chinese Academy of Sciences and Harvard University emerged as core collaboration hubs, with international cooperation network primarily featuring US-China collaboration. Keyword clustering revealed four themes: gene expression, immunotherapy, bioinformatics, and inflammation-related research. Technical bottlenecks included data heterogeneity, insufficient model interpretability and weak cross-dataset generalization capability.

Conclusion: ML-scRNA-seq integration has advanced cellular heterogeneity analysis and precision medicine development. Future directions should optimize deep learning architectures, enhance model generalization capabilities, and promote technical translation through multi-omics and clinical data integration. Interdisciplinary collaboration represents key to overcoming current limitations (e.g., data standardization, algorithm interpretability), ultimately realizing deep integration between single-cell technologies and precision medicine.