CIA: unveiling cellular identities with cluster-independent annotation in single-cell RNA sequencing data for comprehensive cell type characterization and exploration
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.
Authors
- Eugenia Galeota (ORCID: https://orcid.org/0000-0002-4413-7389)
- Renata Grifantini (ORCID: https://orcid.org/0000-0003-0024-3355)
- Francesca Vincenti (ORCID: https://orcid.org/0009-0004-7031-1952)
- Sergio Abrignani (ORCID: https://orcid.org/0000-0002-0794-3285)
- Mattia Battistella (ORCID: https://orcid.org/0009-0006-9236-4398)
- Jole Costanza (ORCID: https://orcid.org/0000-0002-0290-3970)
- Ivan Ferrari (ORCID: https://orcid.org/0000-0003-3746-4143)
- Samuele Notarbartolo (ORCID: https://orcid.org/0000-0001-5910-4910)
- Stefano Biffo (ORCID: https://orcid.org/0000-0002-3780-1050)
- Fédérico Marini (ORCID: https://orcid.org/0000-0003-3252-7758)
- Andrea Gobbini (ORCID: https://orcid.org/0000-0002-8846-4656)
Institutions
- University of Milan (IT)
- Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (IT)
- Istituto Nazionale Genetica Molecolare (IT)
Publication Details
- Journal
- BMC Bioinformatics
- Published
- 2025-12-17
- DOI
- https://doi.org/10.1186/s12859-025-06320-z
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 0.00