title: Equivalence testing in microarray analysis: similarities in the transcriptome of human atherosclerotic and nonatherosclerotic macrophages.
abstract: We focus on similarities in the transcriptome of human Kupffer cells and alveolar, splenic, and atherosclerotic plaque-residing macrophages. We hypothesized that these macrophages share a common expression signature. We performed microarray analysis on mRNA from these subsets (4 patients) and developed a novel statistical method to identify genes with significantly similar expression levels. Phenotypic and functional diversity between macrophage subpopulations reflects their plasticity to respond to microenvironmental signals. Apart from detecting differences in expression profiles, the comparison of the transcriptomes of different macrophage populations may also allow the definition of molecular similarities between these subsets. This new method calculates the maximum difference in gene expression level, based on the estimated confidence interval on that gene's expression variance. We listed the genes by equivalence ranking relative to expression level. FDR estimation was used to determine significance. We identified 500 genes with significantly equivalent expression levels in the macrophage subsets at 5.5% FDR using a confidence level of α = 0.05 for equivalence. Among these are the established macrophage marker CD68, IL1 receptor antagonist, and MHC-related CD1C. These 500 genes were submitted to IPA and GO clustering using DAVID. Additionally, hierarchical clustering of these genes in the Novartis human gene expression atlas revealed a subset of 200 genes specifically expressed in macrophages. Equivalently expressed genes, identified by this new method, may not only help to dissect common molecular mechanisms, but also to identify cell- or condition-specific sets of marker genes that can be used for drug targeting and molecular imaging.
authors.affiliation: Department of Pathology, Cardiovascular Research Institute Maastricht, University of Maastricht, Maastricht;
authors.forename: Wouter J
authors.initials: WJ
authors.lastname: Eijgelaar
authors.forename: Anton J G
authors.initials: AJ
authors.lastname: Horrevoets
authors.forename: Ann-Pascale J J
authors.initials: AP
authors.lastname: Bijnens
authors.forename: Mat J A P
authors.initials: MJ
authors.lastname: Daemen
authors.forename: Wim F J
authors.initials: WF
authors.lastname: Verhaegh
mesh_terms: D050197:Atherosclerosis / Q000235:genetics* / Q000473:pathology*; D016000:Cluster Analysis; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D063990:Gene Ontology; D053263:Gene Regulatory Networks / Q000235:genetics; D006801:Humans; D008264:Macrophages / Q000378:metabolism*; D046228:Microarray Analysis / Q000379:methods*; D013223:Statistics as Topic; D059467:Transcriptome / Q000235:genetics*