title: A class comparison method with filtering-enhanced variable selection for high-dimensional data sets.
abstract: High-throughput molecular analysis technologies can produce thousands of measurements for each of the assayed samples. A common scientific question is to identify the variables whose distributions differ between some pre-specified classes (i.e. are differentially expressed). The statistical cost of examining thousands of variables is related to the risk of identifying many variables that truly are not differentially expressed, and many different multiple testing strategies have been used for the analysis of high-dimensional data sets to control the number of these false positives. An approach that is often used in practice to reduce the multiple comparisons problem is to lessen the number of comparisons being performed by filtering out variables that are considered non-informative 'before' the analysis. However, deciding which and how many variables should be filtered out can be highly arbitrary, and different filtering strategies can result in different variables being identified as differentially expressed. We propose the filtering-enhanced variable selection (FEVS) method, a new multiple testing strategy for identifying differentially expressed variables. This method identifies differentially expressed variables by combining the results obtained using a variety of filtering methods, instead of using a pre-specified filtering method or trying to identify an optimal filtering of the variables prior to class comparison analysis. We prove that the FEVS method probabilistically controls the number of false discoveries, and we show with a set of simulations and an example from the literature that FEVS can be useful for gaining sensitivity for the detection of truly differentially expressed variables.
authors.affiliation: Department of Medical Informatics, University of Ljubljana, Slovenia. Lara.Lusa@mf.uni-lj.si
authors.forename: Lara
authors.initials: L
authors.lastname: Lusa
authors.forename: Edward L
authors.initials: EL
authors.lastname: Korn
authors.forename: Lisa M
authors.initials: LM
authors.lastname: McShane
mesh_terms: D003627:Data Interpretation, Statistical; D015870:Gene Expression; D006801:Humans; D046228:Microarray Analysis / Q000706:statistics & numerical data*
references.citation: Stat Med. 2007 Oct 30;26(24):4428-40
references.pmid: 17357994
references.citation: Proc Natl Acad Sci U S A. 2003 Sep 2;100(18):10393-8
references.pmid: 12917485
references.citation: Cancer Epidemiol Biomarkers Prev. 2006 Jul;15(7):1367-75
references.pmid: 16835338
references.citation: BMC Physiol. 2005 Mar 24;5(1):5
references.pmid: 15790422
references.citation: Cancer Res. 2005 Nov 15;65(22):10602-12
references.pmid: 16288054
references.citation: Bioinformatics. 2003 Dec 12;19(18):2448-55
references.pmid: 14668230
references.citation: Biometrics. 2007 Sep;63(3):806-15
references.pmid: 17825012
references.citation: Genes Chromosomes Cancer. 2003 Jun;37(2):149-58
references.pmid: 12696063
references.citation: Cancer Cell. 2004 Jun;5(6):607-16
references.pmid: 15193263
references.citation: Neoplasia. 2004 Jan-Feb;6(1):1-6
references.pmid: 15068665
references.citation: Lancet. 2003 Aug 2;362(9381):362-9
references.pmid: 12907009
references.citation: Carcinogenesis. 2003 Nov;24(11):1837-45
references.pmid: 12951355
references.citation: Oncogene. 2006 Sep 28;25(44):5994-6002
references.pmid: 16652150
references.citation: Bioinformatics. 2005 Apr 1;21(7):1084-93
references.pmid: 15513985