Gene expression can be quantitatively analyzed by hybridizing fluor-tagged mRNA to targets on a cDNA microarray. Comparison of gene expression levels arising from cohybridized samples is achieved by taking ratios of average expression levels for individual genes. A novel method of image segmentation is provided to identify cDNA target sites and a hypothesis test and confidence interval is developed to quantify the significance of observed differences in expression ratios. In particular, the probability density of the ratio and the maximum-likelihood estimator for the distribution are derived, and an iterative procedure for signal calibration is developed. 1997 Society of Photo-Optical Instrumentation Engineers. [S1083-3668(97)00504-2] Keywords cDNA; microarray; gene expression; image segmentation; Mann--Whitney target detection; ratio density, ratio confidence interval.

Motivation: DNA microarrays are now capable of providing genome-wide patterns of gene expression across many different conditions. The first level of analysis of these patterns requires determining whether observed differences in expression are significant or not. Current methods are unsatisfactory due to the lack of a systematic framework that can accommodate noise, variability, and low replication often typical of microarray data. Results: We develop a Bayesian probabilistic framework for microarray data analysis. At the simplest level, we model log-expression values by independent normal distributions, parameterized by corresponding means and variances with hierarchical prior distributions. We derive point estimates for both parameters and hyperparameters, and regularized expressions for the variance of each gene by combining the empirical variance with a local background variance associated with neighboring genes. An additional hyperparameter, inversely related to the number of empirical observations, determines the strength of the background variance. Simulations show that these point estimates, combined with a t-test, provide a systematic inference approach that compares favorably with simple t-test or fold methods, and partly compensate for the lack of replication. Availability: The approach is implemented in a software called Cyber-T accessible through a Web interface at www.genomics.uci.edu/software.html. The code is available as Open Source and is written in the freely available statistical language R. and Department of Biological Chemistry, College of Medicine, University of California, Irvine. To whom all correspondence should be addressed. Contact: pfbaldi@ics.uci.edu, tdlong@uci.edu. 1

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Motivation: We describe a new approach to the analysis of gene expression data coming from DNA array experiments, using an unsupervised neural network. DNA array technologies allow monitoring thousands of genes rapidly and efficiently. One of the interests of these studies is the search for correlated gene expression patterns, and this is usually achieved by clustering them. The Self-Organising Tree Algorithm, (SOTA) (Dopazo,J. and Carazo,J.M. (1997) J. Mol. Evol., 44, 226--233), is a neural network that grows adopting the topology of a binary tree. The result of the algorithm is a hierarchical cluster obtained with the accuracy and robustness of a neural network. Results: SOTA clustering confers several advantages over classical hierarchical clustering methods. SOTA is a divisive method: the clustering process is performed from top to bottom, i.e. the highest hierarchical levels are resolved before going to the details of the lowest levels. The growing can be stopped at the desired hierarchical level. Moreover, a criterion to stop the growing of the tree, based on the approximate distribution of probability obtained by randomisation of the original data set, is provided. By means of this criterion, a statistical support for the definition of clusters is proposed. In addition, obtaining average gene expression patterns is a built-in feature of the algorithm. Different neurons defining the different hierarchical levels represent the averages of the gene expression patterns contained in the clusters. Since SOTA runtimes are approximately linear with the number of items to be classified, it is especially suitable for dealing with huge amounts of data. The method proposed is very general and applies to any data providing that they can be coded as a series of numbers and t...

The past decade has seen a tremendous growth in the amount of experimental and computational biomedical data, specifically in the areas of Genomics and Proteomics. This growth is accompanied by an accelerated increase in the number of biomedical publications discussing the findings. In the last few years there is a lot of interest within the scientific community in literature-mining tools to help sort through this abundance of literature, and find the nuggets of information most relevant and useful for specific analysis tasks. This paper

The immense volume of data resulting from DNA microarray experiments, accompanied byanincrease in the number of publications discussing gene-related discoveries, presents a major data analysis challenge. Current methods for genome-wide analysis of expression data typically rely on cluster analysis of gene expression patterns. Clustering indeed reveals potentially meaningful relationships among genes, but can not explain the underlying biological mechanisms. In an attempt to address this problem, we have developed a new approach for utilizing the literature in order to establish functional relationships among genes on a genome-wide scale. Our method is based on revealing coherent themes within the literature, using a similarity-based search in document space. Contentbased relationships among abstracts are then translated into functional connections among genes. We describe preliminary experiments applying our algorithm to a database of documents discussing yeast genes...

Microarray expression analysis has become one of the most widely used functional genomics tools. Efficient application of this technique requires the development of robust and reproducible protocols. We have optimized all aspects of the process, including PCR amplification of target cDNA clones, microarray printing, probe labeling, and hybridization, and we have developed strategies for data normalization and analysis. † Address correspondence to:

this paper but has been provided elsewhere (Ratnaparkhi 1997; Manning and Schutze 1999)

Microarray experiments can reveal important information about transcriptional regulation.

There is a substantial interest in implementing bioinformatics technologies that allow the design of oligonucleotides to support the development of microarrays made from short synthetic DNA fragments spotted or in situ synthesized on slides. Ideally, such oligonucleotides should be totally speci®c to their respective targets to avoid any cross-hybridization and should not form stable secondary structures that may interfere with the labeled probes during hybridization. We have developed OligoArray 2.0, a program that designs speci®c oligonucleotides at the genomic scale. It uses a thermodynamic approach to predict secondary structures and to calculate the speci®city of targets on chips for a unique probe in a mixture of labeled probes. Furthermore, OligoArray 2.0 can adjust the oligonucleotide length, according to user input, to ®t a narrow Tm range compatible with hybridization requirements. Combined with on chip oligonucleotide synthesis, this program makes it feasible to perform expression analysis on a genomic scale for any organism for which the genome sequence is known. This is without relying on cDNA or oligonucleotide libraries. OligoArray 2.0 was used to design 75 764 oligonucleotides representing 26 140 transcripts from Arabidopsis thaliana. Among this set, we provide at least one speci®c oligonucleotide for 93 % of these transcripts.