  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.
