scSuperAnnotator: a platform for benchmarking comparison and visualizing automated cellular annotation methods for scRNA-seq data

Abstract

The advent of single-cell RNA-seq has revolutionized the study of gene expression profiles with unparalleled resolution. Accurate identification of cell types from single-cell RNA-seq data is crucial to advance our understanding of disease progression and tumor microenvironments. Although various methods have been proposed to facilitate cell-type annotation, complementing traditional manual approaches, a comprehensive platform that integrates these methods for automated identification is still lacking. To address this gap, we developed scSuperAnnotator, the first online platform that integrates a variety of cell-type identification methods, including both marker gene-based and reference-based approaches, for the automated identification of cell types from single-cell RNA-seq data. A key feature of scSuperAnnotator is its user-friendly interface, which allows researchers to perform one-stop annotation and analyses of single-cell RNA-seq without needing programming expertise. The platform enables users to select appropriate methods and conduct downstream analyses through intuitive, multi-perspective comparisons, streamlining the entire process for greater convenience and efficiency. Furthermore, our platform provides a comprehensive and systematic comparison of existing annotation methods, offering valuable information to researchers.