STEVE: Single-cell Transcriptomics Expression Visualization and Evaluation.

Area of Science:

• Genomics
• Computational Biology
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cell heterogeneity.
• Accurate cell-type annotation is a significant challenge in scRNA-seq data analysis.
• Existing computational tools lack systematic evaluation frameworks for annotation robustness.

Purpose of the Study:

• To introduce STEVE (Single-cell Transcriptomics Expression Visualization and Evaluation), a quantitative framework for assessing cell-type annotation in scRNA-seq.
• To evaluate the accuracy, robustness, and reproducibility of scRNA-seq annotation methods.
• To provide a tool for quantifying annotation uncertainty and enhancing reproducibility.

Main Methods:

• STEVE employs three in silico evaluation modules: Subsampling Evaluation, Novel Cell Evaluation, and Annotation Benchmarking.
• A unified probabilistic framework ensures consistent confidence estimation across all modules.
• Includes a Reference Transfer Annotation module for cross-dataset cell-type mapping.

Main Results:

• Annotation robustness is significantly affected by the chosen annotation method, biological separability, dataset complexity, and batch effects.
• STEVE was evaluated on four independent scRNA-seq datasets with defined cell-type labels.
• Demonstrated STEVE's capability to quantify annotation uncertainty and improve reproducibility.

Conclusions:

• STEVE offers a practical framework for rigorous evaluation of cell-type annotation in scRNA-seq studies.
• The tool aids researchers in improving the accuracy and reproducibility of their analyses.
• STEVE is publicly available on GitHub, promoting wider adoption and further development.