Overall assessment for selected markers from high-throughput data

Woojoo Lee, Donghwan Lee, Yudi Pawitan

Research output: Contribution to journalArticlepeer-review

Abstract

Reproducibility, a hallmark of science, is typically assessed in validation studies. We focus on high-throughput studies where a large number of biomarkers is measured in a training study, but only a subset of the most significant findings is selected and re-tested in a validation study. Our aim is to get the statistical measures of overall assessment for the selected markers, by integrating the information in both the training and validation studies. Naive statistical measures, such as the combined (Formula presented.) -value by conventional meta-analysis, that ignore the non-random selection are clearly biased, producing over-optimistic significance. We use the false-discovery rate (FDR) concept to develop a selection-adjusted FDR (sFDR) as an overall assessment measure. We describe the link between the overall assessment and other concepts such as replicability and meta-analysis. Some simulation studies and two real metabolomic datasets are considered to illustrate the application of sFDR in high-throughput data analyses.

Original languageEnglish
Pages (from-to)5830-5843
Number of pages14
JournalStatistics in Medicine
Volume41
Issue number30
DOIs
StatePublished - 30 Dec 2022

Keywords

  • false discovery rate
  • reproducibility
  • selection adjustment
  • validation study

Fingerprint

Dive into the research topics of 'Overall assessment for selected markers from high-throughput data'. Together they form a unique fingerprint.

Cite this