: Conception, design, and implementation of cDNA microarray experiments present a variety of bioinformatics challenges for biologists and computational scientists. The multiple stages of data acquisition and analysis have motivated the design of Expresso, a system for microarray experiment management. Salient aspects of Expresso include support for clone replication and randomized placement; automatic gridding, extraction of expression data from each spot, and quality monitoring; flexible methods of combining data from individual spots into information about clones and functional categories; and the use of inductive logic programming for higher-level data analysis and mining. The development of Expresso is occurring in parallel with several generations of microarray experiments aimed at elucidating genomic responses to drought stress in loblolly pine seedlings. The current experimental design incorporates 384 pine cDNAs replicated and randomly placed in two specific microarray layouts. We describe the design of Expresso as well as results of analysis with Expresso that suggest the importance of molecular chaperones and membrane transport proteins in mechanisms conferring successful adaptation to long-term drought stress.

Microarray experiments can generate enormous amounts of data, but large datasets are usually inherently complex, and the relevant information they contain can be difficult to extract. For the practicing biologist, we provide an overview of what we believe to be the most important issues that need to be addressed when dealing with microarray data. In a microarray experiment we are simply trying to identify which genes are the most “interesting ” in terms of our experimental question, and these will usually be those that are either overexpressed or underexpressed (upregulated or downregulated) under the experimental conditions. Analysis of the data to find these genes involves first preprocessing of the raw data for quality control, including filtering of the data (e.g., detection of outlying values) followed by standardization of the data (i.e., making the data uniformly comparable throughout the dataset). This is followed by the formal quantitative analysis of the data, which will involve either statistical hypothesis testing or multivariate pattern recognition. Statistical hypothesis testing is the usual approach to “class comparison, ” where several experimental groups are being directly compared. The best approach to this problem is to use analysis of variance, although issues related to multiple hypothesis testing and probability estimation still need to be evaluated. Pattern recognition can involve “class prediction, ” for which a range of supervised multivariate techniques are available, or “class discovery, ” for which an even broader range of unsupervised multivariate techniques have been developed. Each technique has its own limitations, which need to be kept in mind when making a choice from among them. To put these ideas in context, we provide a detailed examination of two specific examples of the analysis of microarray data, both from parasitology, covering many of the most important points raised.

Introduction With progress of the human genome pro ect, thewhol sequences of the human genomewil be determined complnedj in a few years. Functional analona of genes of human genome is now being accelAvqAjI FulfiqvjIAN cDNAclAjN are now beingcoljA9WA because they can be used as a starting material for functional analtio of genes. TheolNF9fiNjIADA cDNAlNAjAF develj ed by Maruyama and Sugano is an e#ective source offulDDAjINv cDNAclAjA [1]. The aim of this study is to construct an integrated database offulAFEjINF cDNA sequences obtained fromoljAWvNqjINF cDNAlNAjEW for the functional analion of genes. For this purpose we introduced a new annotation method using expression profil information obtained from cDNA sequence databases. The database system develj ed here has a function to retrieve the tissue specific genes. Wealv proposed a new method that canstatisticalfi compare the frequency distributions of gene expression over tissues.Finals the valAv9 y of this method was tested usin

One important issue commonly encountered in the analysis of microarray data is to decide which and how many genes should be selected for further studies. For discriminant microarray data analyses based on statistical models, such as the logistic regression models, gene selection can be accomplished by a comparison of the maximum likelihood of the model given the real data, ˆL(D|M), and the expected maximum likelihood of the model given an ensemble of surrogate data with randomly permuted label, ˆL(D0|M). Typically, the computational burden for obtaining ˆL(D0|M) is immense, often exceeding the limits of available computing resources by orders of magnitude. Here, we propose an approach that circumvents such heavy computations by mapping the simulation problem to an extremevalue problem. We present the derivation of an asymptotic distribution of the extreme-value as well as its mean, median, and variance. Using this distribution, we propose two gene selection criteria, and we apply them to two microarray datasets and three classification tasks for illustration. Key words: microarray, gene selection, extreme value distribution, logistic regression. 1.

Motivation. A protocol is described to attach expression patterns to genes represented in a collection of hybridization array experiments. Discrete values are used to provide an easily interpretable description of differential expression. Binning cutoffs for each sample type are chosen automatically, depending on the desired false-positive rate for the predictions of differential expression. Confidence levels are derived for the statement that changes in observed levels represent true changes in expression. We have a novel method for calculating this confidence, which gives better results than the standard methods. Our method reflects the broader change of focus in the field from studying a few genes with many replicates to studying many (possibly thousands) of genes simultaneously, but with relatively few replicates. Our approach differs from standard methods in that it exploits the fact that there are many genes on the arrays. These are used to estimate for each sample type an approp...

Selection of significant genes via expression patterns is an important problem in microarray experiments. Owing to small sample size and the large number of variables (genes), the selection process can be unstable. This paper proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables to specialize the model to a regression setting and uses a Bayesian mixture prior to perform the variable selection. We control the size of the model by assigning a prior distribution over the dimension (number of significant genes) of the model. The posterior distributions of the parameters are not in explicit form and we need to use a combination of truncated sampling and Markov Chain Monte Carlo (MCMC) based computation techniques to simulate the parameters from the posteriors. The Bayesian model is flexible enough to identify significant genes as well as to perform future predictions. The method is applied to cancer classification via cDNA microarrays where the genes BRCA1 and BRCA2 are associated with a hereditary disposition to breast cancer, and the method is used to identify a set of significant genes. The method is also applied successfully to the leukemia data.

With progress of the human genome project, the whole sequences of the human genome will be determined completely in a few years. Functional analysis of genes of human genome is now being accelerated. Full-length cDNA clones are now being collected because they can be used as a starting material for functional analyses of genes. The oligo-capping cDNA library developed by Maruyama

Abstract: The detection of differentially expressed genes from EST data is of importance for the discovery of potential biological or pharmaceutical targets, especially when studying biological processes in less characterized organisms and where large-scale microarrays are not an option. We present a comparison of five different statistical methods for identifying up-regulated genes through pairwise comparison of EST sets, where one of the sets is generated from a treatment and the other one serves as a control. In addition, we specifically address situations where the sets are relatively small (~2,000– 10,000 ESTs) and may differ in size. The methods were tested on both simulated and experimentally derived data, and compared to a collection of cold stress induced genes identified by microarrays. We found that combining the method proposed by Audic and Claverie with Fisher’s exact test and a method based on calculating the difference in relative frequency was the best combination for maximizing the detection of up-regulated genes. We also introduced the use of a flexible cutoff, which takes the size of the EST sets into consideration. This could be considered as an alternative to a static cutoff. Finally, the detected genes showed a low overlap with those identified by microarrays, which indicates, as in previous studies, low overall concordance between the two platforms.

Analysis of mRNA expression and protein abundance data: An approach for the comparison of the enrichment of features in the cellular population of proteins and transcripts

RTOG Headquarters/Statistical Center