The “Window <Emphasis Type="Italic">t</Emphasis> test”: a simple and powerful approach to detect differentially expressed genes in microarray datasets

This work focuses on differential expression analysis of microarray datasets. One way to improve such statistical analyses is to integrate biological information in the design of these analyses. In this paper, we will use the relationship between the level of gene expression and variability. Using this biological information, we propose to integrate the information from multiple genes to get a better estimate of individual gene variance, when a small number of replicates are available, to increase the power of the statistical analysis. We describe a strategy named the “Window t test” that uses multiple genes which share a similar expression level to compute the variance which is then incorporated a classic t test. The performances of this new method are evaluated by comparison with classic and widely-used methods for differential expression analysis (the classic Student t test, the Regularized t test (reg t test), SAM, Limma, LPE and Shrinkage t). In each case tested, the results obtained were at least equivalent to the best performing method and, in most cases, outperformed it. Moreover, the Window t test relies on a very simple procedure requiring small computing power compared with other methods designed for microarray differential expression analysis. Electronic Supplementary Material  Supplementary material is available for this article at and is accessible for authorized users.     

Characterization of variability in large-scale gene expression data: implications for study design

Large-scale gene expression measurement techniques provide a unique opportunity to gain insight into biological processes under normal and pathological conditions. To interpret the changes in expression profiles for thousands of genes, we face the nontrivial problem of understanding the significance of these changes. In practice, the sources of background variability in expression data can be divided into three categories: technical, physiological, and sampling. To assess the relative importance of these sources of background variation, we generated replicate gene expression profiles on high-density Affymetrix GeneChip oligonucleotide arrays, using either identical RNA samples or RNA samples obtained under similar biological states. We derived a novel measure of dispersion in two-way comparisons, using a linear characteristic function. When comparing expression profiles from replicate tests using the same RNA sample (a test for technical variability), we observed a level of dispersion similar to the pattern obtained with RNA samples from replicate cultures of the same cell line (a test for physiological variability). On the other hand, a higher level of dispersion was observed when tissue samples of different animals were compared (an example of sampling variability). This implies that, in experiments in which samples from different subjects are used, the variation induced by the stimulus may be masked by non-stimuli-related differences in the subjects' biological state. These analyses underscore the need for replica experiments to reliably interpret large-scale expression data sets, even with simple microarray experiments.     

Microarray Studies in Bacillus subtilis

This review focuses on the construction of a global, comprehensive understanding of Bacillus subtilis through microarray studies. The microarray studies in B. subtilis were analysed based on the theme of the work, by mentioning the growth media, bioreactor operation conditions, RNA isolation method, number of data points analysed in exponential or stationary phases, compared genotypes, induction and repression ratios, investigated gene(s) and their positive and/or negative influences. Based on the theme and scope of the studies, the articles were reviewed under seven thematic sections, i.e., effects of gene deletion(s) or overexpression, effects of overexression of heterologous genes, comparison of global gene expression between aerobic and anaerobic respiration, effects of temperature change, effects of transported molecules, effects of limitations and stress conditions, and other microarray studies in B. subtilis.     

Analysis of strain and regional variation in gene expression in mouse brain

Background  

We performed a statistical analysis of a previously published set of gene expression microarray data from six different brain regions in two mouse strains. In the previous analysis, 24 genes showing expression differences between the strains and about 240 genes with regional differences in expression were identified. Like many gene expression studies, that analysis relied primarily on ad hoc 'fold change' and 'absent/present' criteria to select genes. To determine whether statistically motivated methods would give a more sensitive and selective analysis of gene expression patterns in the brain, we decided to use analysis of variance (ANOVA) and feature selection methods designed to select genes showing strain- or region-dependent patterns of expression.     

MAPS: a microarray project system for gene expression experiment information and data validation

SUMMARY: MAPS is a MicroArray Project System for management and interpretation of microarray gene expression experiment information and data. Microarray project information is organized to track experiments and results that are: (1) validated by performing analysis on stored replicate gene expression data; and (2) queried according to the biological classifications of genes deposited on microarray chips.     

Sensitivity of 70-mer oligonucleotides and cDNAs for microarray analysis of gene expression in Arabidopsis and its related species

Synthetic oligonucleotides (oligos) represent an attractive alternative to cDNA amplicons for spotted microarray analysis in a number of model organisms, including Arabidopsis, C. elegans, Drosophila, human, mouse and yeast. However, little is known about the relative effectiveness of 60-70-mer oligos and cDNAs for detecting gene expression changes. Using 192 pairs of Arabidopsis thaliana cDNAs and corresponding 70-mer oligos, we performed three sets of dye-swap experiments and used analysis of variance (anova) to compare sources of variation and sensitivities for detecting gene expression changes in A. thaliana, A. arenosa and Brassica oleracea. Our major findings were: (1) variation among different RNA preparations from the same tissue was small, but large variation among dye-labellings and slides indicates the need to replicate these factors; (2) sources of variation were similar for experiments with all three species, suggesting these feature types are effective for analysing gene expression in related species; (3) oligo and cDNA features had similar sensitivities for detecting expression changes and they identified a common subset of significant genes, but results from quantitative RT-PCR did not support the use of one over the other. These findings indicate that spotted oligos are at least as effective as cDNAs for microarray analyses of gene expression. We are using oligos designed from approximately 26,000 annotated genes of A. thaliana to study gene expression changes in Arabidopsis and Brassica polyploids.     

Powers of Multiple-Testing Procedures for Identification of Genes Significantly Differentially Expressed in Microarray Experiments

Because of the high operation costs involved in microarray experiments, the determination of the number of replicates required to detect a gene significantly differentially expressed in a given multiple-testing procedure is of considerable significance. Calculation of power/replicate numbers required in multiple-testing procedures provides design guidance for microarray experiments. Based on this model and by choice of a multiple-testing procedure, expression noises based on permutation resampling can be considerably minimized. The method for mixture distribution model is suitable to various microarray data types obtained from single noise sources, or from multiple noise sources. By using the biological replicate number required in microarray experiments for a given power or by determining the power required to detect a gene significantly differentially expressed, given the sample size, or the best multiple-testing method can be chosen. As an example, a single-distribution model of t-statistic was fitted to an observed microarray dataset of 3 000 genes responsive to stroke in rat, and then used to calculate powers of four popular multiple-testing procedures to detect a gene of an expression change D. The results show that the B-procedure had the lowest power to detect a gene of small change among the multiple-testing procedures, whereas the BH-procedure had the highest power. However, all multiple-testing procedures had the same power to identify a gene having the largest change. Similar to a single test, the power of the BH-procedure to detect a small change does not vary as the number of genes increases, but powers of the other three multiple-testing procedures decline as the number of genes increases.     

Using ribosomal protein genes as reference: a tale of caution

Background

Housekeeping genes are needed in every tissue as their expression is required for survival, integrity or duplication of every cell. Housekeeping genes commonly have been used as reference genes to normalize gene expression data, the underlying assumption being that they are expressed in every cell type at approximately the same level. Often, the terms “reference genes” and “housekeeping genes” are used interchangeably. In this paper, we would like to distinguish between these terms. Consensus is growing that housekeeping genes which have traditionally been used to normalize gene expression data are not good reference genes. Recently, ribosomal protein genes have been suggested as reference genes based on a meta-analysis of publicly available microarray data.

Methodology/Principal Findings

We have applied several statistical tools on a dataset of 70 microarrays representing 22 different tissues, to assess and visualize expression stability of ribosomal protein genes. We confirmed the housekeeping status of these genes, but further estimated expression stability across tissues in order to assess their potential as reference genes. One- and two-way ANOVA revealed that all ribosomal protein genes have significant expression variation across tissues and exhibit tissue-dependent expression behavior as a group. Via multidimensional unfolding analysis, we visualized this tissue-dependency. In addition, we explored mechanisms that may cause tissue dependent effects of individual ribosomal protein genes.

Conclusions/Significance

Here we provide statistical and biological evidence that ribosomal protein genes exhibit important tissue-dependent variation in mRNA expression. Though these genes are most stably expressed of all investigated genes in a meta-analysis they cannot be considered true reference genes.     

Transcriptome‐wide effects of sexual selection on the fate of new mutations

Sexual selection on males is predicted to have widespread effects on genetic variation as a consequence of the pleiotropic allelic effects on sexual and nonsexual traits. We manipulated the opportunity for sexual selection on males during 27 generations of mutation accumulation in inbred lines of Drosophila serrata, and used a microarray platform to investigate the effect of sexual selection on the expression of 2689 genes. While gene expression signal was, on average, higher in the absence of sexual selection, this difference was small (0.1%). In contrast, sexual selection impacted substantially on the mutational variance in gene expression. Over all genes, mutational variance in gene expression was, on average, 42% higher when sexual selection operated than when it was absent. Our results indicate that sexual selection on males can generate widespread effects across the genome. An increase in mutational variance without a corresponding change in mean suggested that most expression traits were unlikely to be under direct sexual selection. Instead, the mutational variance in gene expression traits is consistent with divergence generated by widespread pleiotropic associations with traits affecting male mating success.     

Gene expression variation increase in trisomy 21 tissues

Congenital development disorders with variable severity occur in trisomy 21. However, how these phenotypic abnormalities develop with variations remains elusive. We hypothesize that the differences in euploid gene expression variation among trisomy 21 tissues are caused by the presence of an extra copy of chromosome 21 and may contribute to the phenotypic variations in Down syndrome. We used DNA microarray to measure the differences in gene expression variance between four human trisomy 21 and six euploid amniocytes. The three publicly available data sets of fetal brains, adult brains, and fetal hearts were also analyzed. The numbers of euploid genes with greater variance were significantly higher in all four kinds of trisomy 21 tissues (p < 0.01) than in the corresponding euploid tissues. Seventeen euploid genes with significantly different variance between trisomy 21 and euploid amniocytes were found using the F test. In summary, there is a set of euploid genes that shows greater variance of expression in human trisomy 21 tissues than in euploid tissues. This change may contribute to producing the variable phenotypic abnormalities observed in Down syndrome.     

Extracting biological information from DNA arrays: an unexpected link between arginine and methionine metabolism in Bacillus subtilis

Background  

In global gene expression profiling experiments, variation in the expression of genes of interest can often be hidden by general noise. To determine how biologically significant variation can be distinguished under such conditions we have analyzed the differences in gene expression when Bacillus subtilis is grown either on methionine or on methylthioribose as sulfur source.