title: Application of Discriminant Analysis and Cross-Validation on Proteomics Data.
abstract: High-throughput proteomic experiments have raised the importance and complexity of bioinformatic analysis to extract useful information from raw data. Discriminant analysis is frequently used to identify differences among test groups of individuals or to describe combinations of discriminant variables. However, even in relatively large studies, the number of detected variables typically largely exceeds the number of samples and the classifiers should be thoroughly validated to assess their performance for new samples. Cross-validation is a widely approach when an external validation set is not available. In this chapter, different approaches for cross-validation are presented including relevant aspects that should be taken into account to avoid overly optimistic results and the assessment of the statistical significance of cross-validated figures of merit.
authors.affiliation: Neonatal Research Centre, Health Research Institute La Fe, Valencia, Spain.
authors.forename: Julia
authors.initials: J
authors.lastname: Kuligowski
authors.affiliation: Centre for Biospectroscopy, School of Chemistry, Monash University, Clayton, Australia.
authors.forename: David
authors.initials: D
authors.lastname: Pérez-Guaita
authors.affiliation: Safety and sustainability Division, Leitat Technological Center, Avda. Fernando Abril Martorell, 106, 46026, Valencia, Spain. guira@uv.es.|Analytical Unit, Health Research Institute La Fe, Valencia, Spain. guira@uv.es.
authors.forename: Guillermo
authors.initials: G
authors.lastname: Quintás
mesh_terms: D016002:Discriminant Analysis; D040901:Proteomics / Q000379:methods* / Q000592:standards*; D015203:Reproducibility of Results
keywords: Cross-validation; Discriminant analysis; Double cross-validation; Partial least squares-discriminant analysis; Proteomics