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Background <p>Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of the transcriptional landscape of complex tissues, enabling the discovery of novel cell types and biological functions. However, the identification and classification of cells from scRNA-seq datasets remain significant challenges.</p> Results <p>To address this, we developed a new computational tool called CIA (Cluster Independent Annotation), which accurately identifies cell types across different datasets without requiring a fully annotated reference dataset or complex machine learning processes. Based on predefined cell type signatures, CIA provides a highly user-friendly and practical solution to cell-type and functional annotation of single cells. The CIA framework is implemented in both the Python and R programming languages, making it applicable to all main single-cell analysis frameworks, and it is available under the MIT license with its documentation at the following links: Python package: <a href="https://pypi.org/project/cia-python/">https://pypi.org/project/cia-python/</a>. Python tutorial: <a href="https://cia-python.readthedocs.io/en/latest/tutorial/Cluster_Independent_Annotation.html">https://cia-python.readthedocs.io/en/latest/tutorial/Cluster_Independent_Annotation.html</a>. R package and tutorial: <a href="https://github.com/ingmbioinfo/CIA_R">https://github.com/ingmbioinfo/CIA_R</a>.</p> Conclusions <p>Our results demonstrate that CIA classification performances are comparable to the other state-of-the-art approaches, while requiring a significantly lower computational running time. Overall, CIA simplifies the process of obtaining reproducible signature-based cell assignments that can be easily interpreted through graphical summaries providing researchers with a powerful tool to explore the complex transcriptional landscape of single cells.</p>

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CIA: unveiling cellular identities with cluster-independent annotation in single-cell RNA sequencing data for comprehensive cell type characterization and exploration

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月17日

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  • Ivan Ferrari,
  • Mattia Battistella,
  • Francesca Vincenti,
  • Andrea Gobbini,
  • Federico Marini,
  • Samuele Notarbartolo,
  • Jole Costanza,
  • Stefano Biffo,
  • Renata Grifantini,
  • Sergio Abrignani,
  • Eugenia Galeota

摘要

Background

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of the transcriptional landscape of complex tissues, enabling the discovery of novel cell types and biological functions. However, the identification and classification of cells from scRNA-seq datasets remain significant challenges.

Results

To address this, we developed a new computational tool called CIA (Cluster Independent Annotation), which accurately identifies cell types across different datasets without requiring a fully annotated reference dataset or complex machine learning processes. Based on predefined cell type signatures, CIA provides a highly user-friendly and practical solution to cell-type and functional annotation of single cells. The CIA framework is implemented in both the Python and R programming languages, making it applicable to all main single-cell analysis frameworks, and it is available under the MIT license with its documentation at the following links: Python package: https://pypi.org/project/cia-python/. Python tutorial: https://cia-python.readthedocs.io/en/latest/tutorial/Cluster_Independent_Annotation.html. R package and tutorial: https://github.com/ingmbioinfo/CIA_R.

Conclusions

Our results demonstrate that CIA classification performances are comparable to the other state-of-the-art approaches, while requiring a significantly lower computational running time. Overall, CIA simplifies the process of obtaining reproducible signature-based cell assignments that can be easily interpreted through graphical summaries providing researchers with a powerful tool to explore the complex transcriptional landscape of single cells.