Thu, 21 Aug 2008 09:43:11 BST CiteULike: jyuh's Solé http://www.citeulike.org/user/jyuh/author/Solé CiteULike.org en-gb Copyright © 2004-2008 citeulike.org http://www.citeulike.org/user/jyuh/article/2535357 <i>J Biomed Inform, Vol. 41, No. 1. (February 2008), pp. 33-45.</i><br /><br />A common goal of microarray experiments is to detect genes that are differentially expressed under distinct experimental conditions. Several statistical tests have been proposed to determine whether the observed changes in gene expression are significant. The t-test assigns a score to each gene on the basis of changes in its expression relative to its estimated variability, in such a way that genes with a higher score (in absolute values) are more likely to be significant. Most variants of the t-test use the complete set of genes to influence the variance estimate for each single gene. However, no inference is made in terms of the variability itself. Here, we highlight the problem of low observed variances in the t-test, when genes with relatively small changes are declared differentially expressed. Alternatively, the z-test could be used although, unlike the t-test, it can declare differentially expressed genes with high observed variances. To overcome this, we propose to combine the z-test, which focuses on large changes, with a chi(2) test to evaluate variability. We call this procedure CLEAR-test and we provide a combined p-value that offers a compromise between both aspects. Analysis of three publicly available microarray datasets reveals the greater performance of the CLEAR-test relative to the t-test and alternative methods. Finally, empirical and simulated data analyses demonstrate the greater reproducibility and statistical power of the CLEAR-test and z-test with respect to current alternative methods. In addition, the CLEAR-test improves the z-test by capturing reproducible genes with high variability. CLEAR-test: combining inference for differential expression and variability in microarray data analysis. J Valls M Grau X Solé P Hernández D Montaner J Dopazo MA Peinado G Capellá V Moreno MA Pujana doi:10.1016/j.jbi.2007.05.005 J Biomed Inform, Vol. 41, No. 1. (February 2008), pp. 33-45. 2008-03-15T03:33:57-00:00 2008 J Biomed Inform 1532-0480 41 1 33 45 no-tag http://www.citeulike.org/user/jyuh/article/1092693 <i>Bioinformatics (31 January 2007)</i><br /><br />SUMMARY: The popularization of large scale genotyping projects has led to the widespread adoption of genetic association studies as the tool of choice in the search for single nucleotide polymorphisms (SNPs) underlying susceptibility to complex diseases. Although analysis of individual SNPs is a relatively trivial task to conduct, the large amount of possible combinations of SNPs, diseases and association models to be explored makes it necessary to have an automated tool to perform these analyses routinely. In order to address this issue we developed SNPassoc, an R package to carry out most common analyses when performing whole genome association studies. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy- Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). AVAILABILITY: Package SNPassoc is available at CRAN from http://cran.r-project.org SUPPLEMENTARY INFORMATION: A detailed tutorial is available on Bioinformatics online and in http://davinci.crg.es/estivill_lab/snpassoc. SNPassoc: an R package to perform whole genome association studies. Juan R González Lluís Armengol Xavier Solé Elisabet Guinó Josep M Mercader Xavier Estivill Víctor Moreno Bioinformatics (31 January 2007) 2007-02-07T14:49:01-00:00 2007 Bioinformatics 1460-2059 r snp