CIA (Cluster Independent Annotation)

CIA (Cluster Independent Annotation) is a cutting-edge computational tool designed to accurately classify cells in scRNA-seq datasets using gene signatures. This tool operates without the need for a fully annotated reference dataset or complex machine learning processes, providing a highly user-friendly and practical solution for cell type annotation.

Description

CIA synthesizes the information of each signature expression into a single score value for each cell. By comparing these score values, CIA assigns labels to each cell based on the top-scored signature. CIA can filter scores by their distribution or significance, allowing comparison of genesets with lengths spanning tens to thousands of genes.

CIA is implemented in both R and Python, making it compatible with all major single-cell analysis tools like SingleCellExperiment, Seurat, and Scanpy. This dual compatibility ensures seamless integration into existing workflows.

Key Features

Automatic Annotation: Accurately labels cell types in scRNA-seq datasets based on gene signatures.
Clustering-Free: Operates independently of clustering steps, enabling flexible and rapid data exploration.
Multi-Language Support: Available in both R and Python to suit diverse user preferences.
Compatibility: Integrates with popular single-cell data formats (AnnData, SingleCellExperiment, SeuratObject).
Statistical Analysis: Offers functions for evaluating the quality of signatures and classification performance.
Documentation and Tutorials: Comprehensive guides to facilitate easy adoption and integration into existing workflows.

Documentation

Python Package: CIA Python
Python docs: CIA Python documentation
R Package and Tutorial: CIA R GitHub Repository

Installation

cia package could be installed using pip:

pip install cia-python

To install the github developing version run the following commands:

git clone https://github.com/ingmbioinfo/cia.git

cd cia

pip install -e .

Citation

If you use CIA in your work, please cite our publication as follows:

Ferrari, I., Battistella, M., Vincenti, F. et al. CIA: unveiling cellular identities with cluster-independent annotation in single-cell RNA sequencing data for comprehensive cell type characterization and exploration. BMC Bioinformatics 27, 38 (2026). DOI: 10.1186/s12859-025-06320-z.