Rice gene expression in response to N-acetylchitooligosaccharide elicitor: comprehensive analysis by DNA microarray with randomly selected ESTs

N-acetylchitooligosaccharides are potent elicitors to suspension-cultured rice cells, inducing a set of defense reactions. Expression of defense-related genes is considered to play an important role in defense reactions, and we employed microarray analysis of 8987 randomly selected expressed sequence tags to analyze the changes in gene expression caused by N-acetylchitooctaose. In this experiment, 166 genes were significantly induced and 93 genes were repressed. RNA gel blot analysis of 16 of these genes confirmed the microarray results. Of the 259 ESTs identified as responsive to N-acetylchytooctaose, 18 genes are related to signal transduction, including five calcium-dependent protein kinases (CDPKs). Among these, three novel CDPKs responsive to N-acetylchitooctaose were isolated.     

Technology for microarray analysis of gene expression

The past year has demonstrated the versatility of microarrays for the analysis of whole model-organism genomes and has seen the development of chips to measure the expression of 40,000 human genes. Microarray technology has also become considerably more robust and sensitive. Technology enhancements include the use of noncontact printing methods, improved 2-color sample preparation, and statistically based software for data analysis.     

Weighted analysis of microarray gene expression using maximum-likelihood

MOTIVATION: The numerical values of gene expression measured using microarrays are usually presented to the biological end-user as summary statistics of spot pixel data, such as the spot mean, median and mode. Much of the subsequent data analysis reported in the literature, however, uses only one of these spot statistics. This results in sub-optimal estimates of gene expression levels and a need for improvement in quantitative spot variation surveillance. RESULTS: This paper develops a maximum-likelihood method for estimating gene expression using spot mean, variance and pixel number values available from typical microarray scanners. It employs a hierarchical model of variation between and within microarray spots. The hierarchical maximum-likelihood estimate (MLE) is shown to be a more efficient estimator of the mean than the 'conventional' estimate using solely the spot mean values (i.e. without spot variance data). Furthermore, under the assumptions of our model, the spot mean and spot variance are shown to be sufficient statistics that do not require the use of all pixel data.The hierarchical MLE method is applied to data from both Monte Carlo (MC) simulations and a two-channel dye-swapped spotted microarray experiment. The MC simulations show that the hierarchical MLE method leads to improved detection of differential gene expression particularly when 'outlier' spots are present on the arrays. Compared with the conventional method, the MLE method applied to data from the microarray experiment leads to an increase in the number of differentially expressed genes detected for low cut-off P-values of interest.     

Differential analysis of DNA microarray gene expression data

Here, we review briefly the sources of experimental and biological variance that affect the interpretation of high-dimensional DNA microarray experiments. We discuss methods using a regularized t-test based on a Bayesian statistical framework that allow the identification of differentially regulated genes with a higher level of confidence than a simple t-test when only a few experimental replicates are available. We also describe a computational method for calculating the global false-positive and false-negative levels inherent in a DNA microarray data set. This method provides a probability of differential expression for each gene based on experiment-wide false-positive and -negative levels driven by experimental error and biological variance.     

Quantitative trait associated microarray gene expression data analysis

Selection on phenotypes may cause genetic change. To understand the relationship between phenotype and gene expression from an evolutionary viewpoint, it is important to study the concordance between gene expression and profiles of phenotypes. In this study, we use a novel method of clustering to identify genes whose expression profiles are related to a quantitative phenotype. Cluster analysis of gene expression data aims at classifying genes into several different groups based on the similarity of their expression profiles across multiple conditions. The hope is that genes that are classified into the same clusters may share underlying regulatory elements or may be a part of the same metabolic pathways. Current methods for examining the association between phenotype and gene expression are limited to linear association measured by the correlation between individual gene expression values and phenotype. Genes may be associated with the phenotype in a nonlinear fashion. In addition, groups of genes that share a particular pattern in their relationship to phenotype may be of evolutionary interest. In this study, we develop a method to group genes based on orthogonal polynomials under a multivariate Gaussian mixture model. The effect of each expressed gene on the phenotype is partitioned into a cluster mean and a random deviation from the mean. Genes can also be clustered based on a time series. Parameters are estimated using the expectation-maximization algorithm and implemented in SAS. The method is verified with simulated data and demonstrated with experimental data from 2 studies, one clusters with respect to severity of disease in Alzheimer's patients and another clusters data for a rat fracture healing study over time. We find significant evidence of nonlinear associations in both studies and successfully describe these patterns with our method. We give detailed instructions and provide a working program that allows others to directly implement this method in their own analyses.     

Identification and refinement of two strong constitutive promoters for gene expression system of Schizosaccharomyces pombe

Fission yeast Schizosaccharomyces pombe shares various important properties with higher eukaryotes and is now considered a useful host for elevated production of mammalian proteins for medicinal applications. The full-length nmt1 promoter has been widely used as a strong promoter in S. pombe expression system. In the present study, the promoters of the eno101 and gpd3 genes in S. pombe were identified as strong constitutive promoters. For convenient applications in the plasmids of S. pombe, these promoters were refined to 276-bp eno and 273-bp gpd promoters by deleting undesired sequences and examining the expression of reporter genes including lacZ and xynA. Both the refined eno and gpd promoters provided approximately 1.5-fold higher expression of LacZ than nmt1 promoter. Furthermore, gene expression under the control of the eno or gpd promoter was not repressed by the components of YES medium while nmt1 promoter was inhibited by thiamine in yeast extract. Therefore, both eno and gpd promoters offer opportunities for efficient production of recombinant proteins by S. pombe in high cell-density fermentation.     

AVA: visual analysis of gene expression microarray data

SUMMARY: AVA (Array Visual Analyzer) is a Java program that provides a graphical environment for visualization and analysis of gene expression microarray data. Together with its interactive visualization tools and a variety of built-in data analysis and filtration methods, AVA effectively integrates microarray data normalization, quality assessment, and data mining into one application. AVAILABILITY: The software is freely available for academic users on request from the authors.     

An empirical Bayes' approach to joint analysis of multiple microarray gene expression studies

With the prevalence of gene expression studies and the relatively low reproducibility caused by insufficient sample sizes, it is natural to consider joint analysis that could combine data from different experiments effectively to achieve improved accuracy. We present in this article a model-based approach for better identification of differentially expressed genes by incorporating data from different studies. The model can accommodate in a seamless fashion a wide range of studies including those performed at different platforms by fitting each data with different set of parameters, and/or under different but overlapping biological conditions. Model-based inferences can be done in an empirical Bayes' fashion. Because of the information sharing among studies, the joint analysis dramatically improves inferences based on individual analysis. Simulation studies and real data examples are presented to demonstrate the effectiveness of the proposed approach under a variety of complications that often arise in practice.     

Statistical design and the analysis of gene expression microarray data

Gene expression microarrays are an innovative technology with enormous promise to help geneticists explore and understand the genome. Although the potential of this technology has been clearly demonstrated, many important and interesting statistical questions persist. We relate certain features of microarrays to other kinds of experimental data and argue that classical statistical techniques are appropriate and useful. We advocate greater attention to experimental design issues and a more prominent role for the ideas of statistical inference in microarray studies.