title: Robust semiparametric microarray normalization and significance analysis.
abstract: Microarray technology allows the monitoring of expression levels of thousands of genes simultaneously. A semiparametric location and scale model is proposed to model gene expression levels for normalization and significance analysis purposes. Robust estimation based on weighted least absolute deviation regression and significance analysis based on the weighted bootstrap are investigated. The proposed approach naturally combines normalization and significance analysis, and incorporates the variations due to normalization into the significance analysis properly. A small simulation study is used to compare finite sample performance of the proposed approach with alternatives. We also demonstrate the proposed method with a real dataset.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, Washington 98115, USA. shuangge@u.washington.edu
authors.forename: Shuangge
authors.initials: S
authors.lastname: Ma
authors.forename: Michael R
authors.initials: MR
authors.lastname: Kosorok
authors.forename: Jian
authors.initials: J
authors.lastname: Huang
authors.forename: Hehuang
authors.initials: H
authors.lastname: Xie
authors.forename: Liliana
authors.initials: L
authors.lastname: Manzella
authors.forename: Marcelo Bento
authors.initials: MB
authors.lastname: Soares
mesh_terms: D001699:Biometry; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D010920:Placenta / Q000378:metabolism; D011247:Pregnancy; D012313:RNA / Q000235:genetics
chemical_list: D012313:RNA