LCSB R³
Responsible and Reproducible Research

sccca: Single-Cell Correlation Based Cell Type Annotation#

Authors#

Mohamed Soudy, Sophie Le Bars, Enrico Glaab

Abstract#

The precise identification of cell types in single-cell RNA-Seq datasets is essential for cell type-specific downstream analyses. Current software solutions, which typically focus either on marker-centric or correlation-based approaches, do not offer a cohesive framework that allows for the straightforward comparison and integration of these methodologies. Furthermore, they often require a differential expression analysis, which is prone to inaccuracies stemming from technical and biological variance. To address these challenges, we provide a software tool and comprehensive database, called sccca, that integrates unified cell marker genes from diverse sources and combines marker-centric and correlation-based techniques to enhance cell type identification. sccca enables users to incorporate relevant markers from their datasets and customize the analysis to ensure precise cell type classification.

Availability and Implementation#

sccca is freely available at CRAN at https://cran.r-project.org/web/packages/sccca/index.html and on GitLab https://gitlab.lcsb.uni.lu/bds/sccca.

Database Availability#

The database is freely available at https://sccca.shinyapps.io/UMDB/.