ReviewGene-to-screenFeature selection in clinical proteomics: with great power comes great reproducibility

- Null-hypothesis statistical testing is irreproducible because of P-value variability.
- Irreproducibility can be circumvented by boosting power.
- Increasing sample size and measurement accuracy are unrealistic in practice.
- Use of signal boosting transformations and networks boost power effectively.
- Effect size, confident intervals and cross-validation accuracies complement P-value.

In clinical proteomics, reproducible feature selection is unattainable given the standard statistical hypothesis-testing framework. This leads to irreproducible signatures with no diagnostic power. Instability stems from high P-value variability (p_var), which is inevitable and insolvable. The impact of p_var can be reduced via power increment, for example increasing sample size and measurement accuracy. However, these are not realistic solutions in practice. Instead, workarounds using existing data such as signal boosting transformation techniques and network-based statistical testing is more practical. Furthermore, it is useful to consider other metrics alongside P-values including confidence intervals, effect sizes and cross-validation accuracies to make informed inferences.