scCATCH: Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data

Area of Science:

• Genomics
• Computational Biology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) enables transcriptome profiling for cell classification.
• Current manual cell type annotation in scRNA-seq is subjective and lacks reproducibility.
• Accurate cell type identification is crucial for understanding cellular heterogeneity and disease mechanisms.

Purpose of the Study:

• To introduce scCATCH (single-cell Cluster-based Automatic Annotation Toolkit for Cellular Heterogeneity), an automated tool for cell type annotation.
• To evaluate the performance of scCATCH using benchmark and published scRNA-seq datasets.
• To demonstrate the effectiveness of evidence-based scoring and tissue-specific annotation strategies.

Main Methods:

• Development of the scCATCH toolkit for automated cluster annotation based on gene expression profiles.
• Utilized three benchmark scRNA-seq datasets to validate annotation strategies.
• Compared scCATCH performance against established methods like Seurat for marker gene identification and cell-based annotation.

Main Results:

• scCATCH demonstrated high concordance in cell type identification across datasets.
• The toolkit achieved accurate annotation for 67%-100% (average 83%) of clusters in diverse scRNA-seq datasets.
• scCATCH outperformed Seurat and other cell-based annotation methods in accuracy and reproducibility.

Conclusions:

• scCATCH provides a robust and reproducible method for automated cell type annotation in scRNA-seq data.
• The toolkit facilitates accurate identification of cell identities, aiding mechanistic studies.
• scCATCH has the potential to advance research into disease pathogenesis and progression.