Two-color Microarray Experiments Research Articles

MmPalateMiRNA, an R package compendium illustrating analysis of miRNA microarray data

BackgroundMicroRNAs (miRNAs) constitute the largest family of noncoding RNAs involved in gene silencing and represent critical regulators of cell and tissue differentiation. Microarray expression profiling of miRNAs is an effective means of acquiring genome-level information of miRNA activation and inhibition, as well as the potential regulatory role that these genes play within a biological system. As with mRNA expression profiling arrays, miRNA microarrays come in a variety of platforms from numerous manufacturers, and there are a multitude of techniques available for reducing and analyzing these data.ResultsIn this paper, we present an analysis of a typical two-color miRNA microarray experiment using publicly available packages from R and Bioconductor, the open-source software project for the analysis of genomic data. Covered topics include visualization, normalization, quality checking, differential expression, cluster analysis, miRNA target identification, and gene set enrichment analysis. Many of these tools carry-over from the analysis of mRNA microarrays, but with some notable differences that require special attention. The paper is presented as a “compendium” which, along with the accompanying R package MmPalateMiRNA, contains all of the experimental data and source code to reproduce the analyses contained in the paper.ConclusionsThe compendium presented in this paper will provide investigators with an access point for applying the methods available in R and Bioconductor for analysis of their own miRNA array data.

A-Optimal Designs for Two-Color Microarray Experiments for Treatment-Control and All-to-Next Contrasts

Two-color microarray experiments form an important tool in gene expression analysis. Due to the high costs of microarray experiments it is fundamental to plan these experiments carefully. Therefore, we propose a method to construct A-optimal block designs which can be adapted to construct nearly optimal microarray experiments. This method can be applied to derive A-optimal designs for arbitrary contrast settings. In this article, we focus on treatment control comparisons and all-to-next contrasts. We will show that our designs are more efficient than the designs currently in practice.

Transcriptional responses underlying the hormetic and detrimental effects of the plant secondary metabolite gossypol on the generalist herbivore Helicoverpa armigera

BackgroundHormesis is a biphasic biological response characterized by the stimulatory effect at relatively low amounts of chemical compounds which are otherwise detrimental at higher concentrations. A hormetic response in larval growth rates has been observed in cotton-feeding insects in response to increasing concentrations of gossypol, a toxic metabolite found in the pigment glands of some plants in the family Malvaceae. We investigated the developmental effect of gossypol in the cotton bollworm, Helicoverpa armigera, an important heliothine pest species, by exposing larvae to different doses of this metabolite in their diet. In addition, we sought to determine the underlying transcriptional responses to different gossypol doses.ResultsLarval weight gain, pupal weight and larval development time were measured in feeding experiments and a hormetic response was seen for the first two characters. On the basis of net larval weight gain responses to gossypol, three concentrations (0%, 0.016% and 0.16%) were selected for transcript profiling in the gut and the rest of the body in a two-color double reference design microarray experiment. Hormesis could be observed at the transcript level, since at the low gossypol dose, genes involved in energy acquisition such as β-fructofuranosidases were up-regulated in the gut, and genes involved in cell adhesion were down-regulated in the body. Genes with products predicted to be integral to the membrane or associated with the proteasome core complex were significantly affected by the detrimental dose treatment in the body. Oxidoreductase activity-related genes were observed to be significantly altered in both tissues at the highest gossypol dose.ConclusionsThis study represents the first transcriptional profiling approach investigating the effects of different concentrations of gossypol in a lepidopteran species. H. armigera's transcriptional response to gossypol feeding is tissue- and dose-dependent and involves diverse detoxifying mechanisms not only to alleviate direct effects of gossypol but also indirect damage such as pH disturbance and oxygen radical formation. Genes discovered through this transcriptional approach may be additional candidates for understanding gossypol detoxification and coping with gossypol-induced stress. In a generalist herbivore that has evolved transcriptionally-regulated responses to a variety of different plant compounds, hormesis may be due to a lower induction threshold of growth-promoting, stress-coping responses and a higher induction threshold of detoxification pathways that are costly and cause collateral damage to the cell.

Some Optimal Row-Column Designs

Abstract The optimality of two classes of row-column designs with two rows is established. Studies of row-column designs with two rows arise, for example, from designs of two-color microarray experiments. Each mRNA sample to be labeled with green or red fluorescent dyes and hybridized on a slide is a treatment, each slide defines a column, and the green and red dyes define the rows. We show the optimality of designs such that the treatments appear nearly equally often in the two rows, and the column designs are either balanced incomplete block designs or group-divisible designs with two groups and λ2 = λ1 + 1, two well known classes of optimal block designs.

Robin: An Intuitive Wizard Application for R-Based Expression Microarray Quality Assessment and Analysis

The wide application of high-throughput transcriptomics using microarrays has generated a plethora of technical platforms, data repositories, and sophisticated statistical analysis methods, leaving the individual scientist with the problem of choosing the appropriate approach to address a biological question. Several software applications that provide a rich environment for microarray analysis and data storage are available (e.g. GeneSpring, EMMA2), but these are mostly commercial or require an advanced informatics infrastructure. There is a need for a noncommercial, easy-to-use graphical application that aids the lab researcher to find the proper method to analyze microarray data, without this requiring expert understanding of the complex underlying statistics, or programming skills. We have developed Robin, a Java-based graphical wizard application that harnesses the advanced statistical analysis functions of the R/BioConductor project. Robin implements streamlined workflows that guide the user through all steps of two-color, single-color, or Affymetrix microarray analysis. It provides functions for thorough quality assessment of the data and automatically generates warnings to notify the user of potential outliers, low-quality chips, or low statistical power. The results are generated in a standard format that allows ready use with both specialized analysis tools like MapMan and PageMan and generic spreadsheet applications. To further improve user friendliness, Robin includes both integrated help and comprehensive external documentation. To demonstrate the statistical power and ease of use of the workflows in Robin, we present a case study in which we apply Robin to analyze a two-color microarray experiment comparing gene expression in tomato (Solanum lycopersicum) leaves, flowers, and roots.

Gene Expression Data Analysis Using Closed Itemset Mining for Labeled Data

This article presents an approach to microarray data analysis using discretised expression values in combination with a methodology of closed item set mining for class labeled data (RelSets). A statistical 2 x 2 factorial design analysis was run in parallel. The approach was validated on two independent sets of two-color microarray experiments using potato plants. Our results demonstrate that the two different analytical procedures, applied on the same data, are adequate for solving two different biological questions being asked. Statistical analysis is appropriate if an overview of the consequences of treatments and their interaction terms on the studied system is needed. If, on the other hand, a list of genes whose expression (upregulation or downregulation) differentiates between classes of data is required, the use of the RelSets algorithm is preferred. The used algorithms are freely available upon request to the authors.

ILOOP – a web application for two-channel microarray interwoven loop design

Microarray technology is widely applied to address complex scientific questions. However, there remain fundamental issues on how to design experiments to ensure that the resulting data enables robust statistical analysis. Interwoven loop design has several advantages over other designs. However it suffers in the complexity of design. We have implemented an online web application which allows users to find optimal loop designs for two-color microarray experiments. Given a number of conditions (such as treatments or time points) and replicates, the application will find the best possible design of the experiment and output experimental parameters. It is freely available from .

Statistical Analysis of Efficient Unbalanced Factorial Designs for Two-Color Microarray Experiments

Experimental designs that efficiently embed a fixed effects treatment structure within a random effects design structure typically require a mixed-model approach to data analyses. Although mixed model software tailored for the analysis of two-color microarray data is increasingly available, much of this software is generally not capable of correctly analyzing the elaborate incomplete block designs that are being increasingly proposed and used for factorial treatment structures. That is, optimized designs are generally unbalanced as it pertains to various treatment comparisons, with different specifications of experimental variability often required for different treatment factors. This paper uses a publicly available microarray dataset, as based upon an efficient experimental design, to demonstrate a proper mixed model analysis of a typical unbalanced factorial design characterized by incomplete blocks and hierarchical levels of variability.

Bioinformatics Tools for Plant Genomics

Bioinformatics Tools for Plant Genomics

Correcting for gene-specific dye bias in DNA microarrays using the method of maximum likelihood

In two-color microarray experiments, well-known differences exist in the labeling and hybridization efficiency of Cy3 and Cy5 dyes. Previous reports have revealed that these differences can vary on a gene-by-gene basis, an effect termed gene-specific dye bias. If uncorrected, this bias can influence the determination of differentially expressed genes. We show that the magnitude of the bias scales multiplicatively with signal intensity and is dependent on which nucleotide has been conjugated to the fluorescent dye. A method is proposed to account for gene-specific dye bias within a maximum-likelihood error modeling framework. Using two different labeling schemes, we show that correcting for gene-specific dye bias results in the superior identification of differentially expressed genes within this framework. Improvement is also possible in related ANOVA approaches. A software implementation of this procedure is freely available at http://cellcircuits.org/VERA

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

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. 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 data are available at Bioinformatics online.

What Is the Best Reference RNA? And Other Questions Regarding the Design and Analysis of Two-color Microarray Experiments

The reference design is a practical and popular choice for microarray studies using two-color platforms. In the reference design, the reference RNA uses half of all array resources, leading investigators to ask: What is the best reference RNA? We propose a novel method for evaluating reference RNAs and present the results of an experiment that was specially designed to evaluate three common choices of reference RNA. We found no compelling evidence in favor of any particular reference. In particular, a commercial reference showed no advantage in our data. Our experimental design also enabled a new way to test the effectiveness of pre-processing methods for two-color arrays. Our results favor using intensity normalization and foregoing background subtraction. Finally, we evaluate the sensitivity and specificity of data quality filters, and we propose a new filter that can be applied to any experimental design and does not rely on replicate hybridizations.

Statistical Designs for Two‐Color Spotted Microarray Experiments

Two-color cDNA or oligonucleotide-based spotted microarrays have been commonly used in measuring the expression levels of thousands of genes simultaneously. To realize the immense potential of this powerful new technology, budgeted within limited resources or other constraints, practical designs with high efficiencies are in demand. In this study, we address the design issue concerning the arrangement of the mRNA samples labeled with fluorescent dyes and hybridized on the slides. A normalization model is proposed to characterize major sources of systematic variation in a two-color microarray experiment. This normalization model establishes a connection between designs for two-color microarray experiments with a particular class of classical row-column designs. A heuristic algorithm for constructing A-optimal or highly efficient designs is provided. Statistical optimality results are found for some of the designs generated from the algorithm. It is believed that the constructed designs are the best or very close to the best possible for estimating the relative gene expression levels among the mRNA samples of interest.

Efficient 2k Factorial Designs for Blocks of Size 2 with Microarray Applications

When a two-level design must be run in blocks of size two, there is a unique blocking scheme that enables estimation of all the main effects. Unfortunately, this design does not enable estimation of any two-factor interactions. When the experimental goal is to estimate all main effects and two-factor interactions, it is necessary to combine replicates of the experiment that use different blocking schemes. In this paper, we identify such designs for k ≤ 8 factors that enable estimation of all main effects and two-factor interactions with the fewest number of replications. In addition, we give a construction for general k that gives more economical designs than previously published. Last, we consider issues particular to the design of two-color microarray experiments. For example, we propose a methodology to ensure dye balance in these experiments without increasing the number of replications.

Statistical designs for two-color microarray experiments involving technical replication

In a two-color microarray experiment, we consider the issues of determination of which mRNA samples are to be labeled with which fluorescent dye and which mRNA samples are to be hybridized together on the same slide. Specific attention is given to the test–control experiments whose primary interest lies in comparing several test treatments with a control treatment. A statistical linear model is proposed to characterize two major sources of systematic variation: the variation among distinct slides and that between fluorescent dyes. Furthermore, the possible correlation due to technical replication is also incorporated into the model. A series of A-optimal or highly efficient designs are generated from a heuristic algorithm based on the proposed model. It is shown that the obtained designs are robust not only to the variation of the correlation because of technique replication, but also to the loss of one or two slides. In addition, the comparative experiments involving technical replication are also discussed.

An algorithm for automatic evaluation of the spot quality in two-color DNA microarray experiments

BackgroundAlthough DNA microarray technologies are very powerful for the simultaneous quantitative characterization of thousands of genes, the quality of the obtained experimental data is often far from ideal. The measured microarrays images represent a regular collection of spots, and the intensity of light at each spot is proportional to the DNA copy number or to the expression level of the gene whose DNA clone is spotted. Spot quality control is an essential part of microarray image analysis, which must be carried out at the level of individual spot identification. The problem is difficult to formalize due to the diversity of instrumental and biological factors that can influence the result.ResultsFor each spot we estimate the ratio of measured fluorescence intensities revealing differential gene expression or change in DNA copy numbers between the test and control samples. We also define a set of quality characteristics and a model for combining these characteristics into an overall spot quality value. We have developed a training procedure to evaluate the contribution of each individual characteristic in the overall quality. This procedure uses information available from replicated spots, located in the same array or over a set of replicated arrays. It is assumed that unspoiled replicated spots must have very close ratios, whereas poor spots yield greater diversity in the obtained ratio estimates.ConclusionThe developed procedure provides an automatic tool to quantify spot quality and to identify different types of spot deficiency occurring in DNA microarray technology. Quality values assigned to each spot can be used either to eliminate spots or to weight contribution of each ratio estimate in follow-up analysis procedures.

Evaluation of Experimental Designs for Two-Color cDNA Microarrays

The major goal of two-color cDNA microarray experiments is to measure the relative gene expression level (i.e., relative amount of mRNA) of each gene between samples in studies of gene expression. More specifically, given an N-sample experiment, we need all N(N - 1)/2 relative expression levels of all sample pairs of each gene for identification of the differentially expressed genes and for clustering of gene expression patterns. However, the intensities observed from two-color cDNA microarray experiments do not simply represent the relative gene expression level. They are composed of signal (gene expression level), noise, and other factors. In discussions on the experimental design of two-color cDNA microarray experiments, little attention has been given to the fact that different combinations of test and control samples will produce microarray intensities data with varying intrinsic composition of factors. As a consequence, not all experimental designs for two-color cDNA microarray experiments are able to provide all possible relative gene expression levels. This phenomenon has never been addressed. To obtain all possible relative gene expression levels, a novel method for two-color cDNA microarray experimental design evaluation is necessary that will allow the making of an accurate choice. In this study, we propose a model-based approach to illustrate how the factor composition of microarray intensities changed with different experimental designs in two-color cDNA microarray experiments. By analyzing 12 experimental designs (including 5 general forms), we demonstrate that not all experimental designs are able to provide all possible relative gene expression levels due to the differences in factor composition. Our results indicate that whether an experimental design can provide all possible relative expression levels of all sample pairs for each gene should be the first criterion to be considered in an evaluation of experimental designs for two-color cDNA microarray experiments.

Alberta Regional Meeting 2004

Alberta Regional Meeting 2004

Two-color Microarray Experiments

Microarray gene expression experiments have a complex technical background. Knowledge about certain technical details is inevitable to judge alternatives for both experimental design and analysis. Here, we introduce the necessary details for the so-called two-color microarray experiments and review major sources of technical variance. We follow the sequence of experimental steps during a typical two-color microarray gene expression experiment, stressing decisive points in the choice of technique, experimental handling and biophysical basics. We point out where technical variation is to be expected. Tissue storage, RNA extraction techniques, as well as the microarray hybridization represent major components of technical variance to be considered. Depending on the possibilities for access to the biomedical material under investigation, choice of amplification and labeling techniques can also be decisive to avoid additional technical variance. The two-color microarray experimental approach seeks to avoid a group of probe-level technical biases making use of the advantages of an incomplete block-design. It is worth to know the major sources of technical variance during the typical experimental sequence, both for choice of experimental design and techniques of molecular biology, as well as for the understanding of quality control and normalization approaches. Here, early investments pay at the level of reduced technical variance, allowing for enhanced detection levels for the effects under investigation.