Source-linked AI summary
Essential guidelines for computational method benchmarking
Lukas M. Weber, Wouter Saelens, Robrecht Cannoodt, Charlotte Soneson, Alexander Hapfelmeier, Paul P. Gardner, Anne-Laure Boulesteix, Yvan Saeys, Mark D. Robinson
TL;DR
Researchers need reliable ways to choose among many computational methods, but benchmarking can be misleading without careful design. This review synthesizes practical guidelines across benchmark design, evaluation, interpretation, and reproducibility, emphasizing diverse datasets, suitable metrics, transparent limitations, and tradeoffs. It concludes that high-quality benchmarks can inform users' method selection and developers' research priorities.
Problem
The growing number of computational methods creates a need for rigorous comparisons that support suitable method choices.
Method
The review synthesizes guidelines for designing, performing, interpreting, and reproducing computational benchmarks across the full benchmarking pipeline.
Results
High-quality benchmarks can inform method selection by users and help developers target research efforts.
Takeaways & Limitations
Benchmark design should combine diverse evaluations with transparent discussion of tradeoffs and limitations.
Takeaways & Limitations
Benchmark conclusions are constrained by dataset and evaluation choices, including dataset representativeness, parameter settings, and ground-truth construction.
Abstract
from arXiv · showhide
In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology.
1 Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
The listed affiliations identify participating institutions in Zurich, Ghent, Munich, and Basel.
- The affiliations include Ghent-based centers at VIB and Ghent University.
- The affiliations include Munich institutions focused on medical informatics, statistics, epidemiology, and biomedical research.
- The record gives a current address at the Friedrich Miescher Institute and lists methods, benchmarking, comparison, guidelines, reproducibility, and open science as keywords.
Introduction
The review addresses the growing challenge of choosing among many computational methods by synthesizing guidelines for rigorous, informative, and unbiased benchmarking. It spans benchmark design, evaluation, interpretation, and reproducibility, with examples from computational biology intended to apply more broadly.
- The growing number of computational methods makes method choice both an opportunity and a challenge because it can affect analytical conclusions.
- Benchmarking compares methods using reference datasets and multiple evaluation criteria, with studies conducted by developers, independent groups, or community challenges.
- The review synthesizes essential guidelines across the full benchmarking pipeline, from defining scope through reproducibility practices.
- Examples come from computational biology, but the authors expect most arguments to apply equally to other fields.
Defining the purpose and scope
Benchmark purpose and scope should be defined first because they guide design, method selection, and interpretation. The review distinguishes developer-led, neutral, and community-challenge benchmarks and recommends matching breadth and reporting to the study's purpose.
- Defining the purpose and scope: Benchmark studies may demonstrate a new method, neutrally compare methods, or operate as community challenges.
- Defining the purpose and scope: Neutral benchmarks should be comprehensive where feasible and reduce perceived bias through balanced expertise or author participation.
- Defining the purpose and scope: New-method benchmarks may use a smaller representative comparison set, but must avoid disadvantaging competing methods through unequal tuning.
- Defining the purpose and scope: Results should be interpreted according to the benchmark's purpose, providing user guidance and developer-relevant weaknesses or state-of-the-art comparisons.
- Defining the purpose and scope: For neutral benchmarks, method inclusion should follow justified, non-favoring criteria, while exclusions of widely used methods require explanation.
Selection (or design) of datasets
Reference-dataset selection is central to benchmarking because datasets determine which conditions and truths methods can be evaluated against. Strong benchmarks combine diverse simulated, experimental, and semi-simulated data while acknowledging unavoidable tradeoffs.
- Selection (or design) of datasets: Reference datasets should cover varied conditions and may be simulated, experimental, or semi-simulated.
- Selection (or design) of datasets: Simulated data provide known ground truth for quantitative recovery metrics, but simulations should be checked against relevant properties of real data.
- Selection (or design) of datasets: Experimental data often lack ground truth, so methods may be compared with one another or against accepted methods and gold standards.
- Selection (or design) of datasets: Experimental designs can incorporate ground truth through spike-ins, validation measurements, biological proxies, or related strategies.
- Selection (or design) of datasets: No reference dataset is perfect, so diverse datasets and metrics are needed to distinguish performance and support credible extrapolation to practice.
Parameters and software versions
Parameter settings and software versions can materially affect benchmark results, so neutral studies should use transparent, impartial parameter strategies and current software releases.
- Parameter choices can have a crucial impact on performance, especially for methods with many tunable parameters.Tuning may require substantial effort and expertise.
- Neutral benchmarks should ideally consider a range of parameter values for each method while balancing time and computational-resource constraints.
- Using default parameters reflects typical usage but may be overly simplistic for some neutral benchmarks.The authors used defaults in studies focused on performance for untrained users.
- Parameter selection can introduce bias when a new method is tuned more extensively than competing methods, so the strategy should be reported transparently.
- Software versions can influence results, making reruns necessary when major updates become available during a benchmark.Final results should generally use the latest available versions.
Evaluation criteria: key quantitative performance metrics
Benchmarks should evaluate methods with task-appropriate quantitative metrics and multiple complementary views, because no single metric usually captures performance across methods, tasks, and user priorities.
- Metric choice depends on the method and data, with classification benchmarks commonly using TPR, FPR, and FDR.Clustering benchmarks may use F1 score, adjusted Rand index, normalized mutual information, precision, and recall.
- ROC, TPR-versus-FDR, and PR curves visualize sensitivity-specificity tradeoffs, while area under the ROC or PR curve summarizes them numerically.PR curves can be more informative than ROC curves for imbalanced datasets.
- Continuous-valued outputs can be assessed with metrics such as root mean square error, distance measures, Pearson correlation, and cross-entropy.
- Stability, stochasticity, and robustness metrics do not require a ground truth and can reveal variability across repeated runs or subsampled inputs.Missing values may reflect nonconvergence, excessive runtime, memory limits, or other computational problems.
- Focusing on one metric can be incomplete, so multiple metrics should identify consistently strong methods and expose their different strengths.A single method is unlikely to perform best across all metrics.
Evaluation criteria: secondary measures
Secondary computational and qualitative measures complement performance metrics, helping users choose methods that balance accuracy with feasibility, usability, and maintainability.
- Benchmarks should assess runtime, scalability, computational requirements, user-friendliness, installation, code quality, and documentation alongside quantitative performance.
- Runtimes and scalability can differ by multiple orders of magnitude on the same datasets, and computational requirements may trade off against performance.Systematic scaling tests can vary the number of cells or genes in a single-cell RNA-sequencing dataset.
- Standard package repositories can streamline installation, while freely available and open-source software broadens accessibility and enables adaptation by experienced users.
- High-quality documentation should include help pages and tutorials whose code examples are continually tested, including through continuous integration.
Interpretation, guidelines, and recommendations
Benchmark results should be interpreted and reported for their intended audience through recommendations, accessible visualizations, and transparent limitations, with conclusions that can guide method users and developers.
- Results for method users should become recommendations, potentially using overall or criterion-specific rankings while recognizing that methods may not be directly comparable.
- For method developers, benchmark conclusions can provide guidelines for future development and focus research efforts.
- Limitations must be discussed transparently because undisclosed defaults or ground-truth assumptions can mislead readers and potentially direct research in harmful directions.Examples include using default parameters and manually gated reference cell populations as ground truth.
- Clear visualizations, summary tables, and method-selection flow charts improve accessibility and help readers compare methods for different analyses.
- Interactive online resources can let readers filter metrics and datasets, explore benchmark results, and choose suitable methods.The figure illustrates an interactive website built with the Shiny framework in R.
- Preprints can speed distribution, broaden accessibility, and solicit feedback before peer review.Direct consultation with method authors can also contribute to the reporting process.
Enabling future extensions
Benchmarks should be extensible so researchers can incorporate new methods, datasets, parameter settings, and preprocessing procedures as the field evolves. Continuously updated benchmarks offer particular convenience but require additional effort.
- Public repositories for code and data let researchers extend benchmarks with new methods, datasets, parameter settings, or preprocessing procedures.
- Continuously updated benchmarks are especially convenient, but maintaining them may require significant additional effort.
Reproducible research best practices
Reproducible benchmarks require accessible code and data, complete reporting of parameters and software environments, and tools that preserve computational records. Although some practices demand substantial work, they can facilitate later extensions.
- Access to code and data enables researchers to verify analyses and uncover errors caused by suboptimal method usage.
- Parameter values, random seeds, software versions, and operating-system details should be recorded to ensure complete reproducibility.
- Workflow tools can record software environments, package versions, and parameters while supporting reproducible benchmark setup and online publication.
- Code and data can be shared through general, specialized, or customized repositories when additional flexibility is needed.
- Some reproducibility practices, such as containerization, require substantial additional work but can facilitate later extensions by researchers.
Discussion
The review presents principles for high-quality benchmarking while emphasizing that resource constraints create tradeoffs in their implementation and centrality. Poorly chosen data or metrics can mislead method users and developers, making transparent limitations and critical evaluation important.
- The review identifies key benchmarking principles and summarizes their essentiality alongside associated tradeoffs and pitfalls.
- Subjective dataset or metric choices can bias benchmarks, especially when data are unrepresentative or metrics do not reflect real-world scenarios.
- Misleading benchmarks may cause users to select inappropriate methods and discourage developers from pursuing promising approaches.
- Well-designed benchmarks inform method selection and help developers target research efforts, but require careful attention to design principles.