Abstract
BackgroundHigh-throughput assays are widely used in biological research to select potential targets. One single high-throughput experiment can efficiently study a large number of candidates simultaneously, but is subject to substantial variability. Therefore it is scientifically important to performance quantitative reproducibility analysis to identify reproducible targets with consistent and significant signals across replicate experiments. A few methods exist, but all have limitations.MethodsIn this paper, we propose a new method for identifying reproducible targets. Considering a Bayesian hierarchical model, we show that the test statistics from replicate experiments follow a mixture of multivariate Gaussian distributions, with the one component with zero-mean representing the irreproducible targets.ResultsA target is thus classified as reproducible or irreproducible based on its posterior probability belonging to the reproducible components. We study the performance of our proposed method using simulations and a real data example.ConclusionThe proposed method is shown to have favorable performance in identifying reproducible targets compared to other methods.
Highlights
High-throughput assays are widely used in biological research to select potential targets
Goodman et al [3] provides a survey on the papers with the word reproducibility included in titles, abstracts and keywords, and concludes that the interpretation of reproducibility varies among different papers
In [3], methods reproducibility refers to the provision of enough detail about study procedures and data so the same procedures could, in theory or in actuality, be exactly repeated, such as [1] and [2]; results reproducibility refers to obtaining the same results from the conduct of an independent study whose procedures are as closely matched to the original experiment as possible, such as [4] and [5]; Inferential reproducibility refers to the drawing of qualitatively similar conclusions from
Summary
High-throughput assays are widely used in biological research to select potential targets. One single high-throughput experiment can efficiently study a large number of candidates simultaneously, but is subject to substantial variability. It is scientifically important to performance quantitative reproducibility analysis to identify reproducible targets with consistent and significant signals across replicate experiments. High-throughput assays, such as microarrays, are widely used to effectively select potential targets by studying a large number of candidates in a single experiment. Quantitative analysis for the reproducibility of high-throughput assays is an important exercise for evaluating the reliability and robustness of scientific discoveries across studies. Goodman et al [3] further allies the word reproducibility in the papers and classifies them into three terms: methods reproducibility, results reproducibility and inferential reproducibility. In [3], methods reproducibility refers to the provision of enough detail about study procedures and data so the same procedures could, in theory or in actuality, be exactly repeated, such as [1] and [2]; results reproducibility refers to obtaining the same results from the conduct of an independent study whose procedures are as closely matched to the original experiment as possible, such as [4] and [5]; Inferential reproducibility refers to the drawing of qualitatively similar conclusions from
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