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, 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.