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

ABSTRACT: The outcome of high-throughput biological experiments is affected by many operational factors in the experimental and data-analytical procedures. Understanding how these factors affect the reproducibility of the outcome is critical for establishing workflows that produce replicable discoveries. In this article, we propose a regression framework, based on a novel cumulative link model, to assess the covariate effects of operational factors on the reproducibility of findings from high-throughput experiments. In contrast to existing graphical approaches, our method allows one to succinctly characterize the simultaneous and independent effects of covariates on reproducibility and to compare reproducibility while controlling for potential confounding variables. We also establish a connection be

SUBMITTER: Li Q 

PROVIDER: S-EPMC5975112 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature