Reproducibility Crossroads: Impact of Statistical Choices on Proteomics Functional Enrichment

Area of Science:

• Proteomics
• Bioinformatics
• Statistical Analysis

Background:

• Quantitative proteomics is crucial for identifying differentially expressed proteins.
• Robust statistical methods are essential for accurate differential expression analysis.
• Downstream functional enrichment analysis heavily relies on the quality of differential expression results.

Purpose of the Study:

• To systematically investigate the influence of statistical hypothesis testing approaches and biological relevance criteria on functional enrichment concordance in quantitative proteomics.
• To compare the impact of different statistical methods and relevance definitions on Gene Ontology (GO) and KEGG pathway analysis outcomes.
• To provide insights into optimizing analytical decisions for reproducible proteomics research.

Main Methods:

• Reanalyzed five independent label-free quantitative proteomics datasets.
• Employed diverse frequentist (t-test, Limma, DEqMS, MSstats) and Bayesian (rstanarm) statistical approaches.
• Assessed functional enrichment concordance of GO and KEGG pathways using Jaccard indices and correlation metrics.

Main Results:

• Significant differences in similarity distributions were observed based on statistical methods and relevance criteria.
• Hypothesis testing method variations showed higher concordance than differing biological relevance criteria for GO term overlaps.
• KEGG pathway concordance was more uniform and less sensitive to methodological choices.
• Sensitivity analysis confirmed the robustness of these findings.

Conclusions:

• The definition of biological relevance criteria has a critical impact on functional enrichment outcomes, particularly for GO terms.
• Methodological choices in statistical analysis profoundly influence functional enrichment results in quantitative proteomics.
• Transparency and careful consideration of analytical decisions are vital for reproducible and biologically sound interpretations in proteomics research.