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-occurrenc
Added 2026-04-21