B-T.10: celltypeEnrich: a consensus-based scRNA-seq cluster annotation tool
Authors
Geetu Tuteja
Iowa State University
Keywords
cell-type annotation; RNA-seq; single-cell
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Single-cell RNA sequencing (scRNA-seq) has revolutionized the field of genetics, uncovering nuanced transcriptional profiles that can be lost with whole-tissue RNA sequencing. Despite the advantages, challenges remain in the analysis, interpretation, and reproducibility of scRNA-seq data. One such challenge is cell-type annotation. Traditionally, cluster identities are annotated based on the expression of only a few genes, which is a time-consuming and biased approach. While numerous computational annotation tools have been developed, benchmark studies have shown that they provide inconsistent annotations, limiting their reliability. We developed celltypeEnrich, a web application that determines a consensus cell-type annotation for a given list of input marker genes. The tool uses the hypergeometric test to calculate enrichment of cell-type-specific genes within an input gene list against any combination of 26 reference datasets, including two consortium datasets: Tabula Sapiens and the Human Protein Atlas. Enrichment results across reference datasets are then compared from broad to specific cell-type levels, and recurring matches are used to assign a consensus cell-type annotation. We benchmarked celltypeEnrich using scRNA-seq datasets from three tissues and two species and found that it correctly annotated 63-70% of the clusters using the marker gene lists. In comparison, the three other annotation tools tested were limited by either low accuracy, a lack of models for the tissues evaluated, or the need for parameter optimization. Furthermore, celltypeEnrich's accuracy is maintained when down-sampling input gene lists to 25% of their original size. In summary, celltypeEnrich provides a robust, consistent, and accurate approach for scRNA-seq cluster annotation.
Co-authors: Geetu Tuteja
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