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Related Experiment Video

Updated: Sep 11, 2025

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells
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Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells

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

Xinyu Liu1, Zhen Zhang1, Chao Tan1

  • 1Clinical Medical College & Affiliated Hospital & College of Basic Medicine, Chengdu University, Chengdu, 610081, China.

Hereditas
|August 16, 2025
PubMed
Summary
This summary is machine-generated.

Machine learning and single-cell RNA sequencing (scRNA-seq) advance cellular analysis. Future work should enhance model generalization and integrate multi-omics data for precision medicine.

Keywords:
Bibliometric analysisDeep learningMachine learningRandom forestSingle-cell transcriptomics

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Genomics

Background:

  • Single-cell RNA sequencing (scRNA-seq) enables detailed cellular heterogeneity analysis.
  • Machine learning (ML) is crucial for analyzing scRNA-seq data, including clustering and trajectory inference.
  • A comprehensive bibliometric review is needed to map the evolution and challenges of ML in scRNA-seq.

Purpose of the Study:

  • To conduct a bibliometric and visual analysis of ML applications in single-cell transcriptomics (SCT).
  • To identify technological evolution trends, research hotspots, and future directions.
  • To address methodological evolution, technical challenges, and clinical translation pathways.

Main Methods:

  • Bibliometric and visualization analysis of 3,307 publications from Web of Science Core Collection.
  • Utilized CiteSpace and VOSviewer for systematic review of research trends, contributions, and co-citation relationships.
  • Focused on English articles and reviews, excluding irrelevant document types, to analyze ML in SCT.

Main Results:

  • China and the US lead research output, with China leading in volume and the US in academic influence.
  • Research hotspots include random forest and deep learning models, transitioning towards clinical applications like tumor immune microenvironment analysis.
  • Key themes identified are gene expression, immunotherapy, bioinformatics, and inflammation. Technical challenges include data heterogeneity and model interpretability.

Conclusions:

  • The integration of ML and scRNA-seq drives advancements in cellular heterogeneity analysis and precision medicine.
  • Future research should focus on optimizing deep learning architectures and enhancing model generalization.
  • Interdisciplinary collaboration is vital for overcoming limitations and integrating single-cell technologies with precision medicine.