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In studies that use DNA arrays to assess changes in gene expression, it is preferable to measure the significance of treatment effects on a group of genes from a pathway or functional category such as gene ontology terms (GO terms, http://www.geneontology.org) because this facilitates the interpretation of effects and may markedly increase significance. A modified meta-analysis method to combine p-values was developed to measure the significance of an overall treatment effect on such functionally-defined groups of genes, taking into account the correlation structure among genes. For hypothesis testing that allows gene expression to change in both directions, p-values are calculated under the null distribution generated by a Monte Carlo method. As a test of this procedure, we attempted to distinguish altered pathways in microarray studies performed with Mitochips, oligonucleotide microarrays specific to mitochondrial DNA-encoded transcripts. We found that our analytic method improves the specificity of selection for altered pathways, due to incorporation of the inter-gene correlation structure in each pathway. It is thus a practical method to measure treatment effects on GO groups. In many actual applications, microarray experiments measure treatment effects under complicated design structures and with small sample sizes. For such applications to real data of limited statistical power, and also in computer simulations, we demonstrate that our method gives reasonable test results.

Background: Many researchers are concerned with the comparability and reliability of microarray gene expression data. Recent completion of the MicroArray Quality Control (MAQC) project provides a unique opportunity to assess reproducibility across multiple sites and the comparability across multiple platforms. The MAQC analysis presented for the conclusion of inter- and intra-platform comparability/reproducibility of microarray gene expression measurements is inadequate. We evaluate the reproducibility/comparability of the MAQC data for 12901 common genes in four titration samples generated from five high-density one-color microarray platforms and the TaqMan technology. We discuss some of the problems with the use of correlation coefficient as metric to evaluate the inter- and intra-platform reproducibility and the percent of overlapping genes (POG) as a measure for evaluation of a gene selection procedure by MAQC., Results: A total of 293 arrays were used in the intra- and inter-platform analysis. A hierarchical cluster analysis shows distinct differences in the measured intensities among the five platforms. A number of genes show a small fold-change in one platform and a large fold-change in another platform, even though the correlations between platforms are high. An analysis of variance shows thirty percent of gene expressions of the samples show inconsistent patterns across the five platforms. We illustrated that POG does not reflect the accuracy of a selected gene list. A non-overlapping gene can be truly differentially expressed with a stringent cut, and an overlapping gene can be non-differentially expressed with non-stringent cutoff. In addition, POG is an unusable selection criterion. POG can increase or decrease irregularly as cutoff changes; there is no criterion to determine a cutoff so that POG is optimized., Conclusion: Using various statistical methods we demonstrate that there are differences in the intensities measured by different platforms and different sites within platform. Within each platform, the patterns of expression are generally consistent, but there is site-by-site variability. Evaluation of data analysis methods for use in regulatory decision should take no treatment effect into consideration, when there is no treatment effect, "a fold-change cutoff with a non-stringent p-value cutoff" could result in 100% false positive error selection.

This study describes the development of a mitochondria-specific microarray, MitoChip, to measure transcripts of mitochondria-associated genes in various diseases and drug-induced toxicities in the mouse. The array consists of 542 oligonucleotides that represent genes from the mitochondrial and nuclear genomes associated with mitochondrial structure and functions. The expression of mitochondrial genes was measured in the liver of both p53 haplodeficient (+/-) and wild-type (+/+) C3B6F(1) female mice exposed to antiretroviral agents, Zidovudine (AZT) and Lamivudine (3TC). Among genes whose expression was significantly altered, a set was selected for real-time PCR analysis to verify their differential gene expression. The real-time PCR data confirmed the observations by microarray analysis suggesting that the MitoChip may be an important tool for examining mitochondrial involvement in diseases and drug-induced toxicities.

cDNA arrays allow quantitative measurement of expression levels for thousands of genes simultaneously. The measurements are affected by many sources of variation, and substantial improvements in the precision of estimated effects accompany adjustments for these effects. Two generic nuisance variations, one associated with the magnitude of expression and the other associated with array location, are common in data from filter arrays. Procedures, like normalization using lowess regression, are effective at reducing variation associated with magnitude, and they have been widely adopted. However, variation associated with location has received less attention. Here, a simple, but effective method based on localized median is expounded for dealing with these nuisance effects, and its properties are discussed. The proposed methodology handles location-dependent variation ("splotches") and magnitude-dependent variation (background and/or saturation) effectively. The procedure is related to lowess when implemented to adjust magnitude-dependent variation, and it performs similarly. The proposed methodology is illustrated with data from the National Center for Toxicological Research (NCTR), where treatment differences in levels of mRNA from rat hepatocytes were assessed using 33P-labeled samples hybridized to cDNA spotted arrays. Normalizing intensities by the median-of-subsets removes systematic variation associated with the location of a gene on the array and/or the level of its expression. This procedure is easy to implement using iteratively reweighted least-squares algorithms. Although less sophisticated than lowess, this procedure works nearly as well for normalizing intensities based upon their magnitude. Unlike lowess, it can adjust for location-dependent effects.

An objective of many functional genomics studies is to estimate treatment-induced changes in gene expression. cDNA arrays interrogate each tissue sample for the levels of mRNA for hundreds to tens of thousands of genes, and the use of this technology leads to a multitude of treatment contrasts. By-gene hypotheses tests evaluate the evidence supporting no effect, but selecting a significance level requires dealing with the multitude of comparisons. The p-values from these tests order the genes such that a p-value cutoff divides the genes into two sets. Ideally one set would contain the affected genes and the other would contain the unaffected genes. However, the set of genes selected as affected will have false positives, i.e., genes that are not affected by treatment. Likewise, the other set of genes, selected as unaffected, will contain false negatives, i.e., genes that are affected. A plot of the observed p-values (1 - p) versus their expectation under a uniform [0, 1] distribution allows one to estimate the number of true null hypotheses. With this estimate, the false positive rates and false negative rates associated with any p-value cutoff can be estimated. When computed for a range of cutoffs, these rates summarize the ability of the study to resolve effects. In our work, we are more interested in selecting most of the affected genes rather than protecting against a few false positives. An optimum cutoff, i.e., the best set given the data, depends upon the relative cost of falsely classifying a gene as affected versus the cost of falsely classifying a gene as unaffected. We select the cutoff by a decision-theoretic method analogous to methods developed for receiver operating characteristic curves. In addition, we estimate the false discovery rate and the false nondiscovery rate associated with any cutoff value. Two functional genomics studies that were designed to assess a treatment effect are used to illustrate how the methods allowed the investigators to determine a cutoff to suit their research goals.

Motivation: A microarray experiment is a multi-step process, and each step is a potential source of variation. There are two major sources of variation: biological variation and technical variation. This study presents a variance-components approach to investigating animal-to-animal, between-array, within-array and day-to-day variations for two data sets. The first data set involved estimation of technical variances for pooled control and pooled treated RNA samples. The variance components included between-array, and two nested within-array variances: between-section (the upper- and lower-sections of the array are replicates) and within-section (two adjacent spots of the same gene are printed within each section). The second experiment was conducted on four different weeks. Each week there were reference and test samples with a dye-flip replicate in two hybridization days. The variance components included week-to-week, animal-to-animal and between-array and within-array variances., Results: We applied the linear mixed-effects model to quantify different sources of variation. In the first data set, we found that the between-array variance is greater than the between-section variance, which, in turn, is greater than the within-section variance. In the second data set, for the reference samples, the week-to-week variance is larger than the between-array variance, which, in turn, is slightly larger than the within-array variance. For the test samples, the week-to-week variance has the largest variation. The animal-to-animal variance is slightly larger than the between-array and within-array variances. However, in a gene-by-gene analysis, the animal-to-animal variance is smaller than the between-array variance in four out of five housekeeping genes. In summary, the largest variation observed is the week-to-week effect. Another important source of variability is the animal-to-animal variation. Finally, we describe the use of variance-component estimates to determine optimal numbers of animals, arrays per animal and sections per array in planning microarray experiments.

Modest changes in gene expression of three-fold or less might be expected after mild to moderate neurotoxic exposure to classes of compounds, such as the substituted amphetamines, or at time points that are weeks after more severe neurotoxic exposures. When many genes appear to change expression by less than two-fold, it is crucial to run several pairs of arrays and use statistical analysis to determine which genes are really changing. This limits the number of genes that have to undergo the time consuming task of performing RT-PCR to validate change in expression levels. We describe here methods for statistically determining which genes are being expressed above background levels. These methods are used to compare expression differences among the striatum, parietal cortex, posterior lateral amygdaloid nucleus, and substantia nigra brain regions, all of which differ significantly in their gene expression profiles. In these comparisons, it was possible to distinguish differences among hundreds of genes with manageable estimated false discovery rates. The effect of amphetamine treatment on gene expression in posterior lateral amygdaloid nucleus was also evaluated. The expression data indicate that many genes have changed, but in this case it is more difficult to separate affected genes from false positives. The optimum list has 50 genes, of which 32% are expected to be false positives.