The current landscape and emerging challenges of benchmarking single-cell methods.

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Single-cell sequencing technologies generate vast amounts of data, driving the development of numerous computational methods.
• Benchmarking studies are crucial for evaluating the performance and reliability of these single-cell analysis tools.
• The rapid increase in single-cell benchmarking publications necessitates a comprehensive assessment of the field's current status.

Purpose of the Study:

• To provide a quantitative summary of the landscape of single-cell benchmarking studies.
• To identify key performance metrics and under-examined aspects in current benchmarking research.
• To address challenges in synthesizing knowledge across studies and preventing researcher burnout.

Main Methods:

• Conducted a systematic literature search to identify relevant benchmarking papers.
• Assessed 282 publications, including 130 benchmark-only studies and 152 method development papers with benchmarking components.
• Analyzed performance across nine categories, incorporating dataset influence, method robustness, and downstream evaluation.

Main Results:

• Identified a surge in single-cell benchmarking studies, creating a need for comprehensive overview.
• Highlighted performance variations across different computational methods and datasets.
• Revealed under-explored areas in benchmarking, such as the impact of dataset characteristics and method robustness.

Conclusions:

• The current state of single-cell benchmarking requires a more unified approach to knowledge synthesis.
• Addressing challenges like 'benchmarking fatigue' is essential for sustainable research progress.
• Adopting a community-led research paradigm and establishing best practices are crucial for advancing the field.