A methodology for global validation of microarray experiments

Background  

DNA microarrays are popular tools for measuring gene expression of biological samples. This ever increasing popularity is ensuring that a large number of microarray studies are conducted, many of which with data publicly available for mining by other investigators. Under most circumstances, validation of differential expression of genes is performed on a gene to gene basis. Thus, it is not possible to generalize validation results to the remaining majority of non-validated genes or to evaluate the overall quality of these studies.     

Statistical implications of pooling RNA samples for microarray experiments

Background  

Microarray technology has become a very important tool for studying gene expression profiles under various conditions. Biologists often pool RNA samples extracted from different subjects onto a single microarray chip to help defray the cost of microarray experiments as well as to correct for the technical difficulty in getting sufficient RNA from a single subject. However, the statistical, technical and financial implications of pooling have not been explicitly investigated.     

Assessment of repeated microarray experiments using mixed tissue RNA reference samples

Genome-scale gene expression technologies are increasingly being applied for biological research as a whole and toxicological screening in particular. In order to monitor data quality and process drift, we adopted the use of two rat-tissue mixtures (brain, liver, kidney, and testis) previously introduced as RNA reference samples. These samples were processed every time a microarray experiment was hybridized, thereby verifying the comparability of the resulting expression data for cross-study comparison. This study presents the analysis of 21 technical replicates of these two mixed-tissue samples using Affymetrix RAE230_2 GeneChip over a period of 12 months. The results show that detection sensitivity, measured by the number of present and absent sequences, is robust, and data correlation, indicated by scatter plots, varies little over time. Receiver operating characteristic (ROC) curves show the sensitivity and specificity of the current measurements are consistent with arrays previously classified as well performing. Overall, this paper shows that the inclusion of standard samples during microarray labeling and hybridization experiments is useful to benchmark the performance of microarray experiments over time and allows discovery of any process drift that, if it occurs, may confound the comparison of these datasets.     

Systematic analysis of T7 RNA polymerase basedin vitrolinear RNA amplification for use in microarray experiments

Background  

The requirement of a large amount of high-quality RNA is a major limiting factor for microarray experiments using biopsies. An average microarray experiment requires 10–100 μg of RNA. However, due to their small size, most biopsies do not yield this amount. Several different approaches for RNA amplificationin vitrohave been described and applied for microarray studies. In most of these, systematic analyses of the potential bias introduced by the enzymatic modifications are lacking.     

Spotting effect in microarray experiments

Background  

Microarray data must be normalized because they suffer from multiple biases. We have identified a source of spatial experimental variability that significantly affects data obtained with Cy3/Cy5 spotted glass arrays. It yields a periodic pattern altering both signal (Cy3/Cy5 ratio) and intensity across the array.     

Monitoring gene expression profile changes in ovarian carcinomas using cDNA microarray

The development of cancer is the result of a series of molecular changes occurring in the cell. These events lead to changes in the expression level of numerous genes that result in different phenotypic characteristics of tumors. In this report we describe the assembly and utilization of a 5766 member cDNA microarray to study the differences in gene expression between normal and neoplastic human ovarian tissues. Several genes that may have biological relevance in the process of ovarian carcinogenesis have been identified through this approach. Analyzing the results of microarray hybridizations may provides new leads for tumor diagnosis and intervention.     

A global approach to identify differentially expressed genes in cDNA (two-color) microarray experiments

MOTIVATION: Currently most of the methods for identifying differentially expressed genes fall into the category of so called single-gene-analysis, performing hypothesis testing on a gene-by-gene basis. In a single-gene-analysis approach, estimating the variability of each gene is required to determine whether a gene is differentially expressed or not. Poor accuracy of variability estimation makes it difficult to identify genes with small fold-changes unless a very large number of replicate experiments are performed. RESULTS: We propose a method that can avoid the difficult task of estimating variability for each gene, while reliably identifying a group of differentially expressed genes with low false discovery rates, even when the fold-changes are very small. In this article, a new characterization of differentially expressed genes is established based on a theorem about the distribution of ranks of genes sorted by (log) ratios within each array. This characterization of differentially expressed genes based on rank is an example of all-gene-analysis instead of single gene analysis. We apply the method to a cDNA microarray dataset and many low fold-changed genes (as low as 1.3 fold-changes) are reliably identified without carrying out hypothesis testing on a gene-by-gene basis. The false discovery rate is estimated in two different ways reflecting the variability from all the genes without the complications related to multiple hypothesis testing. We also provide some comparisons between our approach and single-gene-analysis based methods. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.     

Bayesian hierarchical model for identifying changes in gene expression from microarray experiments.

Recent developments in microarrays technology enable researchers to study simultaneously the expression of thousands of genes from one cell line or tissue sample. This new technology is often used to assess changes in mRNA expression upon a specified transfection for a cell line in order to identify target genes. For such experiments, the range of differential expression is moderate, and teasing out the modified genes is challenging and calls for detailed modeling. The aim of this paper is to propose a methodological framework for studies that investigate differential gene expression through microarrays technology that is based on a fully Bayesian mixture approach (Richardson and Green, 1997). A case study that investigated those genes that were differentially expressed in two cell lines (normal and modified by a gene transfection) is provided to illustrate the performance and usefulness of this approach.     

Evidence for changes in messenger RNA content related to tomato fruit ripening

Poly(A)-containing mRNA was purified from tomato fruits and translated in a wheat germ in vitro protein-synthesizing system. Comparison of the protein products produced in response to mRNA samples from unripe and ripening fruits provides evidence for changes in the amounts of mRNA coding for specific proteins during ripening.     

Characterizing dye bias in microarray experiments

MOTIVATION: Spot intensity serves as a proxy for gene expression in dual-label microarray experiments. Dye bias is defined as an intensity difference between samples labeled with different dyes attributable to the dyes instead of the gene expression in the samples. Dye bias that is not removed by array normalization can introduce bias into comparisons between samples of interest. But if the bias is consistent across samples for the same gene, it can be corrected by proper experimental design and analysis. If the dye bias is not consistent across samples for the same gene, but is different for different samples, then removing the bias becomes more problematic, perhaps indicating a technical limitation to the ability of fluorescent signals to accurately represent gene expression. Thus, it is important to characterize dye bias to determine: (1) whether it will be removed for all genes by array normalization, (2) whether it will not be removed by normalization but can be removed by proper experimental design and analysis and (3) whether dye bias correction is more problematic than either of these and is not easily removable. RESULTS: We analyzed two large (each >27 arrays) tissue culture experiments with extensive dye swap arrays to better characterize dye bias. Indirect, amino-allyl labeling was used in both experiments. We found that post-normalization dye bias that is consistent across samples does appear to exist for many genes, and that controlling and correcting for this type of dye bias in design and analysis is advisable. The extent of this type of dye bias remained unchanged under a wide range of normalization methods (median-centering, various loess normalizations) and statistical analysis techniques (parametric, rank based, permutation based, etc.). We also found dye bias related to the individual samples for a much smaller subset of genes. But these sample-specific dye biases appeared to have minimal impact on estimated gene-expression differences between the cell lines.     

Estimating p-values in small microarray experiments

MOTIVATION: Microarray data typically have small numbers of observations per gene, which can result in low power for statistical tests. Test statistics that borrow information from data across all of the genes can improve power, but these statistics have non-standard distributions, and their significance must be assessed using permutation analysis. When sample sizes are small, the number of distinct permutations can be severely limited, and pooling the permutation-derived test statistics across all genes has been proposed. However, the null distribution of the test statistics under permutation is not the same for equally and differentially expressed genes. This can have a negative impact on both p-value estimation and the power of information borrowing statistics. RESULTS: We investigate permutation based methods for estimating p-values. One of methods that uses pooling from a selected subset of the data are shown to have the correct type I error rate and to provide accurate estimates of the false discovery rate (FDR). We provide guidelines to select an appropriate subset. We also demonstrate that information borrowing statistics have substantially increased power compared to the t-test in small experiments.     

Monocistronic messenger RNA in yeast

We have determined the rate of polypeptide chain synthesis on different size polysomes in yeast. The completion time for the average polypeptide chain in vivo at 23 °C is two minutes by this technique and is in good agreement with values we have determined by other independent methods.These kinetic experiments indicate that the average size of a nascent polypeptide chain on a polysome is directly related to the size of the polysome. This demonstrates that in the simple eucaryotic organism, Saccharomyces cerevisiae, mRNA is monocistronic in the sense that each mRNA molecule codes for one protein molecule which is released intact from the ribosome upon completion. The pattern of amino acid incorporation into Escherichia coli polysomes is distinctly different. These findings have a number of interesting implications for the genetics of the lower eucaryotes and indicate that the cellular mechanisms of control and co-ordination in yeast may differ from those found in procaryotes and may be similar to cellular mechanisms of control for mammalian cells.     

Autoimmune epitopes in messenger RNA

Patients with systemic autoimmune disorders produce autoantibodies against sequence-specific conformational RNA epitopes on U1 snRNA, 28S rRNA, and transfer RNAs. The molecular basis for immunological reactivity with these highly abundant and stable RNAs is not understood. Here, we report the existence of discrete RNA epitopes in messenger RNAs that are generally less abundant and less stable than snRNAs and tRNAs. An iterative selection and amplification procedure using pooled autoimmune patient sera identified immunoreactive mRNA species. Following deconvolution of the pools to identify the reactive sera, several mRNAs recognized by these autoantibodies were cloned and sequenced. Detailed analysis using one particular serum indicated reactivity against the messages encoding alternative splicing factor (ASF/SF2) and calmodulin. Deletion and site-directed mutagenesis determined that an epitope recognized by this serum is located in a 17-base stem-loop structure common to both messages. This serum was then used to immunoprecipitate native mRNAs encoding ASF/SF2 and calmodulin from total HeLa cell RNA. Our results demonstrate that despite its low abundance and instability, messenger RNA is capable of reacting with autoantibodies generated during an autoimmune response. These data are consistent with direct presentation as a model to explain the generation of RNA conformation-specific autoantibodies.     

Auxin-induced changes in the population of translatable messenger RNA in elongating maize coleoptile sections

In-vitro translation products of polyadenylated RNA from untreated and indole-3-acetic acid (IAA)-treated elongating sections of maize (Zea mays L.) coleoptiles were analyzed by twodimensional polyacrylamide gel electrophoresis. Treatment with IAA results in an increased amount of at least four in-vitro translation products. The amounts of two of these translation products are increased within 10 min of IAA treatment.Abbreviation IAA indole-3-acetic acid