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Related Experiment Video

Updated: Aug 24, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Overall assessment for selected markers from high-throughput data.

Woojoo Lee¹, Donghwan Lee², Yudi Pawitan³

¹Department of Public Health Science, Graduate School of Public Health, Seoul National University, Seoul, Republic of Korea.

Statistics in Medicine

|October 21, 2022

Summary

This summary is machine-generated.

Reproducibility in high-throughput studies needs careful assessment. A new selection-adjusted false-discovery rate (sFDR) method provides unbiased statistical measures by integrating training and validation data.

Keywords:

false discovery rate reproducibility selection adjustment validation study

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Area of Science:

Biostatistics
Genomics
Bioinformatics

Background:

Reproducibility is crucial in high-throughput studies, where a subset of significant biomarkers from training data is validated.
Conventional meta-analysis methods can yield biased, over-optimistic significance by ignoring non-random selection.
Accurate statistical assessment is needed to integrate information from both training and validation phases.

Approach:

Developed a selection-adjusted false-discovery rate (sFDR) metric based on the false-discovery rate (FDR) concept.
Integrated information from both training and validation datasets for a comprehensive assessment.
Explored the relationship between overall assessment, replicability, and meta-analysis.

Key Points:

Naive statistical measures are biased when selection is non-random.
sFDR offers a statistically sound method for assessing selected markers in high-throughput studies.
The approach accounts for the selection process, providing more reliable significance measures.

Conclusions:

sFDR provides a robust measure for the overall assessment of selected biomarkers in high-throughput studies.
This method corrects for bias introduced by non-random selection, enhancing scientific rigor.
Validated using simulations and real-world metabolomic datasets, demonstrating practical applicability.