CIA: a Cluster Independent Annotation method to investigate cell identities in scRNA-seq data

Abstract

AbstractSingle-cell RNA sequencing (scRNA-seq) has revolutionised our ability to explore the transcriptional landscape of complex tissues and uncover novel cell types and biological functions. However, the task of identifying and classifying cells from scRNA-seq datasets remains a major challenge. To address this issue, we developed a new computational tool called CIA (Cluster Independent Annotation) that can accurately identify cell types across different datasets without the need for a training dataset or complex machine learning processes. Based on predefined cell type signatures, CIA provides a highly user-friendly and practical solution to functional annotation of single cells. Our results demonstrate that CIA outperforms other state-of-the-art approaches, while also having significantly lower computational running times. Overall, CIA simplifies the process of obtaining graphical representations of signature enrichment scores and classification results, providing researchers with a powerful tool to explore the complex transcriptional landscape of single cells.For further details, see tutorials (https://github.com/ingmbioinfo/cia/tree/master/tutorial).