Scholarlink

<p>Accurate cell type classification is critical for downstream analysis in single-cell RNA sequencing (scRNA-seq). Most existing methods rely on a single type of feature representation—such as statistical, information theory, matrix factorization, or deep learning-based features. However, each captures different aspects of the data, and no single feature type can fully represent the complex differences between cell types. Moreover, naïvely concatenating multiple features may introduce redundancy or noise, reducing model performance. To address these challenges, we propose scMFF, which is a multiple feature fusion framework that integrates four features and explores six fusion strategies in combination with various classifiers for single-cell type classification. Comprehensive evaluations on 42 disease-related datasets and an external COVID-19 dataset demonstrate that scMFF outperforms single-feature approaches in terms of performance and stability, providing a reliable and effective solution for scRNA-seq data analysis.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

scMFF: a machine learning framework with multiple feature fusion strategies for cell type identification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月18日

查看全文
  • Nan Sun,
  • Yu Wang,
  • Xiang Shi,
  • Dengcheng Yang,
  • Rongling Wu,
  • Stephen S.-T. Yau

摘要

Accurate cell type classification is critical for downstream analysis in single-cell RNA sequencing (scRNA-seq). Most existing methods rely on a single type of feature representation—such as statistical, information theory, matrix factorization, or deep learning-based features. However, each captures different aspects of the data, and no single feature type can fully represent the complex differences between cell types. Moreover, naïvely concatenating multiple features may introduce redundancy or noise, reducing model performance. To address these challenges, we propose scMFF, which is a multiple feature fusion framework that integrates four features and explores six fusion strategies in combination with various classifiers for single-cell type classification. Comprehensive evaluations on 42 disease-related datasets and an external COVID-19 dataset demonstrate that scMFF outperforms single-feature approaches in terms of performance and stability, providing a reliable and effective solution for scRNA-seq data analysis.