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.
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.
- 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.
- Python Package: CIA Python
- Python docs: CIA Python documentation
- R Package and Tutorial: CIA R GitHub Repository
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 .
If you use CIA in your work, please cite our publication as follows:
Ferrari I, Battistella M, Vincenti F, Gobbini A, Notarbartolo S, Costanza J, Biffo S, Grifantini R, Abrignani S, Galeota E. (2023). "CIA: a Cluster Independent Annotation method to investigate cell identities in scRNA-seq data". bioRxiv. doi: 10.1101/2023.11.30.569382.