title: Partial least squares and logistic regression random-effects estimates for gene selection in supervised classification of gene expression data.
abstract: Our main interest in supervised classification of gene expression data is to infer whether the expressions can discriminate biological characteristics of samples. With thousands of gene expressions to consider, a gene selection has been advocated to decrease classification by including only the discriminating genes. We propose to make the gene selection based on partial least squares and logistic regression random-effects (RE) estimates before the selected genes are evaluated in classification models. We compare the selection with that based on the two-sample t-statistics, a current practice, and modified t-statistics. The results indicate that gene selection based on logistic regression RE estimates is recommended in a general situation, while the selection based on the PLS estimates is recommended when the number of samples is low. Gene selection based on the modified t-statistics performs well when the genes exhibit moderate-to-high variability with moderate group separation. Respecting the characteristics of the data is a key aspect to consider in gene selection.
authors.affiliation: Department of Statistics, University of Leeds, Leeds, United Kingdom. a.gusnanto@leeds.ac.uk
authors.forename: Arief
authors.initials: A
authors.lastname: Gusnanto
authors.forename: Alexander
authors.initials: A
authors.lastname: Ploner
authors.forename: Farag
authors.initials: F
authors.lastname: Shuweihdi
authors.forename: Yudi
authors.initials: Y
authors.lastname: Pawitan
mesh_terms: D015870:Gene Expression; D006801:Humans; D016018:Least-Squares Analysis; D016013:Likelihood Functions; D016015:Logistic Models; D008223:Lymphoma / Q000235:genetics; D012641:Selection, Genetic
keywords: Filtering; Gene selection; Logistic regression; Partial least squares; Random effects; Supervised classification