Abstract not found

High density oligonucleotide expression arrays are widely used in many areas of biomedical research. Affymetrix GeneChip arrays are the most popular. In the Affymetrix system, a fair amount of further pre-processing and data reduction occurs following the image processing

Abstract not found

additional microarray platforms and data formats

Algorithms for performing feature extraction and normalization on high-density oligonucleotide gene expression arrays, have not been fully explored, and the impact these algorithms have on the downstream analysis is not well understood. Advances in such low-level analysis methods are essential to increase the sensitivity and specificity of detecting whether genes are present and/or differentially expressed. We have developed and implemented a number of algorithms for the analysis of expression array data in a software application, the DNA-Chip Analyzer (dChip). In this report, we describe the algorithms for feature extraction and normalization, and present validation data and comparison results with some of the algorithms currently in use.

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.

Microarrays measure values that are approximately proportional to the numbers of copies of different mRNA molecules in samples. Due to technical difficulties, the constant of proportionality between the measured intensities and the numbers of mRNA copies per cell is unknown and may vary for different arrays. Usually, the data are normalized (i.e., array-wise multiplied by appropriate factors) in order to compensate for this effect and to enable informative comparisons between different experiments. Centralization is a new two-step method for the computation of such normalization factors that is both biologically better motivated and more robust than standard approaches. First, for each pair of arrays the quotient of the constants of proportionality is estimated. Second, from the resulting matrix of pairwise quotients an optimally consistent scaling of the samples is computed. Contact: Alexander.Zien@gmd.de

Knowledge discovery in high dimensional data is a challenging enterprise, but new visual analytic tools appear to offer users remarkable powers if they are ready to learn new concepts and interfaces. Our 3-year effort to develop versions of the Hierarchical Clustering Explorer (HCE) began with building an interactive tool for exploring clustering results. It expanded, based on user needs, to include other potent analytic and visualization tools for multivariate data, especially the rank-by-feature framework. Our own successes using HCE provided some testimonial evidence of its utility, but we felt it necessary to get beyond our subjective impressions. This paper presents an evaluation of the Hierarchical Clustering Explorer (HCE) using three case studies and an email user survey (n=57) to focus on skill acquisition with the novel concepts and interface for the rank-by-feature framework. Knowledgeable and motivated users in diverse fields provided multiple perspectives that refined our understanding of strengths and weaknesses. A user survey confirmed the benefits of HCE, but gave less guidance about improvements. Both evaluations suggested improved training methods.

A variety of new procedures have been devised to handle the two sample comparison (e.g., tumor versus normal tissue) of gene expression values as measured with microarrays. Such new methods are required in part because of some defining characteristics of microarray-based studies: (i) the very large number of genes contributing expression measures which far exceeds the number of samples (observations) available, and (ii) the fact that by virtue of pathway/network relationships, the gene expression measures tend to be highly correlated. These concerns are exacerbated in the regression setting, where the objective is to relate gene expression, simultaneously for multiple genes, to some external outcome or phenotype. Correspondingly, several methods have been recently proposed for addressing these issues. We briefly critique some of these methods prior to a detailed evaluation of gene harvesting. This reveals that gene harvesting, without additional constraints, can yield artifac-tual solutions. Results obtained employing such constraints motivate the use of regularized regression procedures such as the lasso, least angle regression, and support vector machines. Model selection and solution multiplicity issues are also discussed. The methods are evaluated using a microarray-based study of cardiomyopathy in transgenic mice. Key words: cardiomyopathy, covariance inflation criterion, gene harvesting, lasso, least angle regres-sion, microarray, model selection, support vector machine. 1

Normalization of microarray data is essential for removing experimental biases and revealing meaningful biological results. Motivated by a problem of normalizing microarray data, a semilinear in-slide model (SLIM) has been proposed. To aggregate information from other arrays, SLIM is generalized to account for across-array information, resulting in an even more dynamic semiparametric regression model. This model can be used to normalize microarray data even when there is no replication within an array. We demonstrate that this semiparametric model has a number of interesting features. The parametric component and the nonparametric component that are of primary interest can be consistently estimated, the former having a parametric rate and the latter having a nonparametric rate, whereas the nuisance parameters cannot be consistently estimated. This is an interesting extension of the partial consistent phenomena, which itself is of theoretical interest. The asymptotic normality for the parametric component and the rate of convergence for the nonparametric component are established. The results are augmented by simulation studies and illustrated by an application to the cDNA microarray analysis of neuroblastoma cells in response to the macrophage migration inhibitory factor.