Zhijin Wu Johns Hopkins Bloomberg School of Public Health 615 North Wolfe Street zwu@jhsph.edu Rafael A. Irizarry Johns Hopkins Bloomberg School of Public Health 615 North Wolfe Street rafa@jhu.edu ABSTRACT High density oligonucleotide expression arrays are a widely used tool for the measurement of gene expression on a large scale. A#ymetrix GeneChip arrays appear to dominate this market. These arrays use short oligonucleotides to probe for genes in an RNA sample. Due to optical noise, nonspecific hybridization, probe-specific e#ects, and measurement error, ad-hoc measures of expression, that summarize probe intensities, can lead to imprecise and inaccurate results. Various researchers have demonstrated that expression measures based on simple statistical models can provide great improvements over the ad-hoc procedure o#ered by A#ymetrix. Recently, physical models based on molecular hybridization theory, have been proposed as useful tools for prediction of, for example, non-specific hybridization. These physical models show great potential in terms of improving existing expression measures. In this paper we suggest that the system producing the measured intensities is too complex to be fully described with these relatively simple physical models and we propose empirically motivated stochastic models that compliment the above mentioned molecular hybridization theory to provide a comprehensive description of the data. We discuss how the proposed model can be used to obtain improved measures of expression useful for the data analysts.

Motivation: Inference about differential expression is a typical objective when analyzing gene expression data. Recently, Bayesian hierarchical models have become increasingly popular for this type of problem. The two most common hierarchical models are the hierarchical Gamma–Gamma (GG) and Lognormal–Normal (LNN) models. However, to facilitate inference, some unrealistic assumptions have been made. One such assumption is that of a common coefficient of variation across genes, which can adversely affect the resulting inference. Results: In this paper, we extend both the GG and LNN modeling frameworks to allow for gene-specific variances and propose EM based algorithms for parameter estimation. The proposed methodology is evaluated on three experimental datasets: one cDNA microarray experiment and two Affymetrix spike-in experiments. The two extended models significantly reduce the false positive rate while keeping a high sensitivity when compared to the originals. Finally, using a simulation study we show that the new frameworks are also more robust to model misspecification. Availability: The R code for implementing the proposed methodology can be downloaded at

Systematic profiling of expressed gene products represents a promising research strategy for elucidating the molecular phenotypes of islet cells. To this end, we have combined complementary genomic and proteomic methods to better assess the molecular composition of murine pancreatic islet glucagon-producing �TC-1 cells as a model system, with the expectation of bypassing limitations inherent to either technology alone. Gene expression was measured with an Affymetrix MG_U74Av2 oligonucleotide array, while protein expression was examined by performing high-resolution gel-free shotgun MS/MS on a nuclear-enriched cell extract. Both analyses were carried out in triplicate to control for experimental variability. Using a stringent detection p value cutoff of 0.04, 48 % of all potential mRNA transcripts were predicted to be expressed

© 2008 Binder and Preibisch; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License

Invited Review-R1 DNA microarray represents a powerful tool in biomedical discoveries. Harnessing the potential of this technology depends on the development and appropriate use of data mining and statistical tools. Significant current advances have made microarray data mining more versatile. Researchers are no longer limited to default choices that generate suboptimal results. Conflicting results in repeated experiments can be resolved through attention to the statistical details. In the current dynamic environment, there are many choices and potential pitfalls for researchers who intend to incorporate microarrays as a research tool. This review is intended to provide a simple framework to understand the choices and identify the pitfalls. Specifically, this review article discusses the choice of microarray platform, preprocessing raw data, differential expression and validation, clustering, annotation and functional characterization of genes, and pathway construction in light of emergent concepts and tools.

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Abstract. Currently, the method most used for gene detection calls on Affymetrix oligonucleotide arrays is provided as part of the MAS5.0 software. The MAS method uses Wilcoxon statistics for determining presence-absence (MAS-P/A) calls. It is known that MAS-P/A is limited by its need to use both perfect match (PM) and mismatch (MM) probe data in order to call a gene present or absent. A considerable amount of recent research has convincingly shown that using MM data in gene expression analysis is problematic. The RMA method, which uses PM data only, is one method that has been developed in response to this. However, there is no publicly available method outside of MAS-P/A to establish presence or absence of genes from microarray data. It seems desirable to decouple the method used to generate gene expression values from the method used to make gene detection calls. We have therefore developed a statistical method in R, called Presence-Absence calls with Negative Probesets (PANP) which uses sets of Affymetrix-reported probes with no known hybridization partners on three chip sets: HG-U133A, HG-U133B, and HG-U133 Plus 2. This

by thermodynamic intensity correction

Improving comparability between microarray probe

1.1 Installation on Microsoft Windows............... 9