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

ABSTRACT: 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)

SUBMITTER: Singh R 

PROVIDER: S-EPMC9039958 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature