title: Feature selection in clinical proteomics: with great power comes great reproducibility.
abstract: 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.
authors.affiliation: School of Pharmaceutical Science and Technology, Tianjin University, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.affiliation: School of Pharmaceutical Science and Technology, Tianjin University, China.
authors.forename: Andrew C-H
authors.initials: AC
authors.lastname: Sue
authors.affiliation: School of Pharmaceutical Science and Technology, Tianjin University, China; Department of Bioengineering, Tianjin University, China; Department of Computer Science, National University of Singapore, Singapore. Electronic address: wilson.goh@tju.edu.cn.
authors.forename: Wilson W B
authors.initials: WWB
authors.lastname: Goh
mesh_terms: D003657:Decision Making; D006801:Humans; D040901:Proteomics / Q000706:statistics & numerical data*; D015203:Reproducibility of Results