Overall assessment for selected markers from high-throughput data
- 주제(키워드) false discovery rate , reproducibility , selection adjustment , validation study
- 등재 SCIE, SCOPUS
- 발행기관 John Wiley and Sons Ltd
- 발행년도 2022
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000203261
- 본문언어 영어
- Published As https://doi.org/10.1002/sim.9596
- PubMed https://pubmed.ncbi.nlm.nih.gov/36270585
초록/요약
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. © 2022 John Wiley & Sons Ltd.
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