title: HTself: self-self based statistical test for low replication microarray studies.
abstract: Different statistical methods have been used to classify a gene as differentially expressed in microarray experiments. They usually require a number of experimental observations to be adequately applied. However, many microarray experiments are constrained to low replication designs for different reasons, from financial restrictions to scarcely available RNA samples. Although performed in a high-throughput framework, there are few experimental replicas for each gene to allow the use of traditional or state-of-art statistical methods. In this work, we present a web-based bioinformatics tool that deals with real-life problems concerning low replication experiments. It uses an empirically derived criterion to classify a gene as differentially expressed by combining two widely accepted ideas in microarray analysis: self-self experiments to derive intensity-dependent cutoffs and non-parametric estimation techniques. To help laboratories without a bioinformatics infrastructure, we implemented the tool in a user-friendly website (http://blasto.iq.usp.br/~rvencio/HTself).
authors.affiliation: BIOINFO-USP-Núcleo de Pesquisas em Bioinformática, Universidade de São Paulo, São Paulo, Brazil. rvencio@vision.ime.usp.br
authors.forename: Ricardo Z N
authors.initials: RZ
authors.lastname: Vêncio
authors.forename: Tie
authors.initials: T
authors.lastname: Koide
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D020407:Internet; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012984:Software; D044167:Xylella / Q000235:genetics