Segmented correspondence curve regression model for quantifying reproducibility of high-throughput experiments

TL;DR

This paper introduces a segmented regression model to quantify how operational factors affect reproducibility in high-throughput experiments, accounting for heterogeneity across significance levels, and demonstrates its effectiveness through simulations and real data analysis.

Contribution

The paper develops a novel segmented regression model based on rank concordance to analyze operational factors' effects on reproducibility at different significance levels.

Findings

The method accurately detects change points in operational effects.

It outperforms existing methods in model fitting and error control.

Application reveals sequencing depth impacts reproducibility.

Abstract

The reliability of a high-throughput biological experiment relies highly on the settings of the operational factors in its experimental and data-analytic procedures. Understanding how operational factors influence the reproducibility of the experimental outcome is critical for constructing robust workflows and obtaining reliable results. One challenge in this area is that candidates at different levels of significance may respond to the operational factors differently. To model this heterogeneity, we develop a novel segmented regression model, based on the rank concordance between candidates from different replicate samples, to characterize the varying effects of operational factors for candidates at different levels of significance. A grid search method is developed to identify the change point in response to the operational factors and estimate the covariate effects accounting for the change. A sup-likelihood-ratio-type test is proposed to test the existence of a change point. Simulation studies show that our method yields a well-calibrated type I error, is powerful in detecting the difference in reproducibility, and achieves a better model fitting than the existing method. An application on a ChIP-seq dataset reveals interesting insights on how sequencing depth affects the reproducibility of experimental results, demonstrating the usefulness of our method in designing cost-effective and reliable high-throughput workflows.