Annotation of cell types (ACT): a convenient web server for cell type annotation.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell sequencing advances biological research but generates massive datasets.
• Accurate cell type annotation is crucial for interpreting these datasets.
• Current manual annotation methods are time-consuming and require specialized knowledge.

Purpose of the Study:

• To develop an automated and efficient method for cell type annotation in single-cell data.
• To create a user-friendly tool that overcomes the limitations of manual annotation.
• To accelerate the analysis and interpretation of large-scale single-cell datasets.

Main Methods:

• Constructed a hierarchical cell marker map from extensive literature curation (approx. 7000 publications).
• Developed WISE (weighted and integrated gene set enrichment), a novel method integrating marker prevalence and gene expression order.
• Integrated the marker map and WISE into a web server named ACT (Automated Cell Type annotation).

Main Results:

• The ACT web server provides interactive hierarchy maps and statistical information from a simple gene list input.
• ACT demonstrated superior performance compared to state-of-the-art methods in benchmarking analyses.
• Case studies showed ACT rapidly and accurately annotated cell clusters, identifying multi-level cell types.

Conclusions:

• ACT offers a powerful, knowledge-based resource and an intuitive graphical interface for cell type annotation.
• This tool significantly accelerates cell type identification in single-cell research.
• ACT is expected to become a widely adopted tool in the field of single-cell genomics.