title: Combining P values to improve classification of differential gene  expression in the HTself software.
abstract: HTself is a web-based bioinformatics tool designed to  deal with the classification of differential gene expression in low replication  microarray studies. It is based on a statistical test that uses self-self  experiments to derive intensity-dependent cutoffs. We developed an extension of  HTself, originally released in 2005, by calculating P values instead of using a  fixed acceptance level α. As before, the statistic used to compute  single-spot P values is obtained from the Gaussian kernel density estimator method  applied to self-self data. Different spots corresponding to the same biological  gene (replicas) give rise to a set of independent P values that can be combined  by well-known statistical methods. The combined P value can be used to decide  whether a gene can be considered differentially expressed or not. HTself2 is a  new version of HTself that uses P values combination. It is implemented as a  user-friendly desktop application to help laboratories without a bioinformatics  infrastructure.
authors.affiliation: Departamento de Ciências da Computação, Instituto de Matemática e Estatística, Universidade de São Paulo, São Paulo, Brasil.
authors.forename: D A
authors.initials: DA
authors.lastname: Cortez
authors.forename: A P
authors.initials: AP
authors.lastname: Tonon
authors.forename: P
authors.initials: P
authors.lastname: Colepicolo
authors.forename: R Z N
authors.initials: RZ
authors.lastname: Vêncio
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000145:classification*; D015233:Models, Statistical; D000461:Rhodophyta / Q000235:genetics; D012984:Software; D013997:Time Factors