A regression framework for assessing covariate effects on the reproducibility of high-throughput experiments

Area of Science:

• Genomics and Bioinformatics
• Statistical Modeling in Biology

Background:

• High-throughput experiments generate vast data, susceptible to operational variations.
• Reproducibility is crucial for reliable scientific discoveries, yet often challenged by experimental and analytical factors.

Purpose of the Study:

• To develop a statistical framework for assessing how operational factors influence the reproducibility of high-throughput experimental results.
• To provide a method for comparing reproducibility while accounting for confounding variables.

Main Methods:

• Proposed a novel regression framework based on a cumulative link model.
• Established a connection between the proposed model and Archimedean copula models for enhanced interpretation and guidance.
• Utilized simulations to evaluate the model's performance.

Main Results:

• The proposed regression framework effectively characterizes simultaneous and independent covariate effects on reproducibility.
• The method demonstrated calibrated type I error rates in simulations.
• Outperformed existing measures of agreement in detecting differences in reproducibility.

Conclusions:

• The novel cumulative link model offers a robust approach to quantify the impact of operational factors on reproducibility in high-throughput studies.
• The established copula connections provide valuable insights for model selection and interpretation in reproducibility research.
• The method's utility was demonstrated in real-world ChIP-seq and microarray studies, enhancing the reliability of biological findings.