Assessing reproducibility of high-throughput experiments in the case of missing data.

High-throughput experiments are an essential part of modern biological and biomedical research. The outcomes of high-throughput biological experiments often have a lot of missing observations due to signals below detection levels. For example, most single-cell RNA-seq (scRNA-seq) protocols experience high levels of dropout due to the small amount of starting material, leading to a majority of reported expression levels being zero. Though missing data contain information about reproducibility, they are often excluded in the reproducibility assessment, potentially generating misleading assessments. In this article, we develop a regression model to assess how the reproducibility of high-throughput experiments is affected by the choices of operational factors (eg, platform or sequencing depth) when a large number of measurements are missing. Using a latent variable approach, we extend correspondence curve regression, a recently proposed method for assessing the effects of operational factors to reproducibility, to incorporate missing values. Using simulations, we show that our method is more accurate in detecting differences in reproducibility than existing measures of reproducibility. We illustrate the usefulness of our method using a single-cell RNA-seq dataset collected on HCT116 cells. We compare the reproducibility of different library preparation platforms and study the effect of sequencing depth on reproducibility, thereby determining the cost-effective sequencing depth that is required to achieve sufficient reproducibility.