Normalization of two-channel microarrays accounting for experimental design and intensity-dependent relationships

In normalizing two-channel expression arrays, the ANOVA approach explicitly incorporates the experimental design in its model, and the MA plot-based approach accounts for intensity-dependent biases. However, both approaches can lead to inaccurate normalization in fairly common scenarios. We propose a method called efficient Common Array Dye Swap (eCADS) for normalizing two-channel microarrays that accounts for both experimental design and intensity-dependent biases. Under reasonable experimental designs, eCADS preserves differential expression relationships and requires only a single array per sample pair.     

Extending the loop design for two-channel microarray experiments

The loop design of Kerr and Churchill is a clever application of incomplete blocks of size 2 to two-channel microarray experiments. In this paper, we extend the loop design to include more replicates, biological and technical replication, multi-factor experiments, and blocking. Loop and extended loop designs are shown to be more efficient than the reference design for any given number of arrays. We also show that adding new treatments to a loop design requires the same number of additional arrays as adding treatments to a reference design, with a greater gain in power. Given the flexibility of extended loop designs and their power, we propose that these should be the designs of choice for most experiments using two-channel microarrays.     

Reliability of gene expression ratios for cDNA microarrays in multiconditional experiments with a reference design

In a typical gene expression profiling experiment with multiple conditions, a common reference sample is used for co-hybridization with the samples to yield expression ratios. Differential expression for any other sample pair can then be calculated by assembling the ratios from their hybridizations with the reference. In this study we test the validity of this approach. Differential expression of a sample pair (i, j) was obtained in two ways: directly, by hybridizations of sample i versus j, and indirectly, by multiplying the expression ratios for hybridizations of sample i versus pool and pool versus sample j. We performed gene expression profiling using amphibian embryos (Xenopus laevis). Every sample combination of four different stages and a pool was profiled. Direct and indirect values were compared and used as the quality criterion for the data. Based on this criterion, 82% of all ratios were found to be sufficiently accurate. To increase the reliability of the signals, several widely used filtering techniques were tested. Filtering by differences of repeated hybridizations was found to be the optimal filter. Finally, we compared microarray-based gene expression profiles with the corresponding expression patterns obtained by whole-mount in situ hybridizations, resulting in a 90% correspondence.     

A new approach to intensity-dependent normalization of two-channel microarrays

A two-channel microarray measures the relative expression levels of thousands of genes from a pair of biological samples. In order to reliably compare gene expression levels between and within arrays, it is necessary to remove systematic errors that distort the biological signal of interest. The standard for accomplishing this is smoothing "MA-plots" to remove intensity-dependent dye bias and array-specific effects. However, MA methods require strong assumptions, which limit their general applicability. We review these assumptions and derive several practical scenarios in which they fail. The "dye-swap" normalization method has been much less frequently used because it requires two arrays per pair of samples. We show that a dye-swap is accurate under general assumptions, even under intensity-dependent dye bias, and that a dye-swap removes dye bias from a single pair of samples in general. Based on a flexible model of the relationship between mRNA amount and single-channel fluorescence intensity, we demonstrate the general applicability of a dye-swap approach. We then propose a common array dye-swap (CADS) method for the normalization of two-channel microarrays. We show that CADS removes both dye bias and array-specific effects, and preserves the true differential expression signal for every gene under the assumptions of the model.     

An experimental loop design for the detection of constitutional chromosomal aberrations by array CGH

Background  

Comparative genomic hybridization microarrays for the detection of constitutional chromosomal aberrations is the application of microarray technology coming fastest into routine clinical application. Through genotype-phenotype association, it is also an important technique towards the discovery of disease causing genes and genomewide functional annotation in human. When using a two-channel microarray of genomic DNA probes for array CGH, the basic setup consists in hybridizing a patient against a normal reference sample. Two major disadvantages of this setup are (1) the use of half of the resources to measure a (little informative) reference sample and (2) the possibility that deviating signals are caused by benign copy number variation in the "normal" reference instead of a patient aberration. Instead, we apply an experimental loop design that compares three patients in three hybridizations.     

Experimental design for gene expression microarrays

We examine experimental design issues arising with gene expression microarray technology. Microarray experiments have multiple sources of variation, and experimental plans should ensure that effects of interest are not confounded with ancillary effects. A commonly used design is shown to violate this principle and to be generally inefficient. We explore the connection between microarray designs and classical block design and use a family of ANOVA models as a guide to choosing a design. We combine principles of good design and A-optimality to give a general set of recommendations for design with microarrays. These recommendations are illustrated in detail for one kind of experimental objective, where we also give the results of a computer search for good designs.     

Multifactorial experimental design and the transitivity of ratios with spotted DNA microarrays

Background  

Multifactorial experimental designs using DNA microarrays are becoming increasingly common, but the extent of the transitivity of cDNA microarray expression measurements across multiple samples has yet to be explored.     

Development of a spot reliability evaluation score for DNA microarrays

We developed a reliability index named SRED (Spot Reliability Evaluation Score for DNA microarrays) that represents the probability that the calibrated gene expression level from a DNA microarray would be less than a factor of 2 different from that of quantitative real-time polymerase chain reaction assays whose dynamic quantification range is treated statistically to be similar to that of the DNA microarray. To define the SRED score, two parameters, the reproducibility of measurement value and the relative expression value were selected from nine candidate parameters. The SRED score supplies the probability that the expression level in each spot of a microarray is less than a certain-fold different compared to other expression profiling data, such as QRT-PCR. This score was applied to 1,500,000 points of the expression profile in the RIKEN Expression Array Database.     

OligoArray: genome-scale oligonucleotide design for microarrays

SUMMARY: OligoArray is a program that computes gene specific and secondary structure free oligonucleotides for genome-scale oligonucleotide microarray construction or other applications. AVAILABILITY: The program code is distributed under the GNU General Public License and is freely available for non-profit use via request from the authors.     

Homogeneous versus heterogeneous probes for microbial ecological microarrays

Microbial ecological microarrays have been developed for investigating the composition and functions of microorganism communities in environmental niches. These arrays include microbial identification microarrays, which use oligonucleotides, gene fragments or microbial genomes as probes. In this article, the advantages and disadvantages of each type of probe are reviewed. Oligonucleotide probes are currently useful for probing uncultivated bacteria that are not amenable to gene fragment probing, whereas the functional gene fragments amplified randomly from microbial genomes require phylogenetic and hierarchical categorization before use as microbial identification probes, despite their high resolution for both specificity and sensitivity. Until more bacteria are sequenced and gene fragment probes are thoroughly validated, heterogeneous bacterial genome probes will provide a simple, sensitive and quantitative tool for exploring the ecosystem structure.     

Strategy for the design of custom cDNA microarrays

DNA microarrays are valuable but expensive tools for expression profiling of cells, tissues, and organs. The design of custom microarrays leads to cost reduction without necessarily compromising their biological value. Here we present a strategy for designing custom cDNA microarrays and constructed a microarray for mouse immunology research (ImmunoChip). The strategy used interrogates expressed sequence tag databases available in the public domain but overcomes many of the problems encountered. Immunologically relevant clusters were selected based on the expression of expressed sequence tags in relevant libraries. Selected clusters were organized in modules, and the best representative clones were identified. When tested, this microarray was found to have minimal clone identity errors or phage contamination and identified molecular signatures of lymphoid cell lines. Our proposed design of custom microarrays avoids probe redundancy, allows the organization of the chip to optimize chip production, and reduces microarray production costs. The strategy described is also useful for the design of oligonucleotide microarrays.     

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.     

Technical evaluation of cDNA microarrays

The aims were to evaluate the common reference design approach in microarray experiments and to evaluate the technical performance and the normalisation of cDNA microarrays with a limited number of spots. Total RNA from 3 normal and 3 tumour sample biopsies were used for synthesis of amino-allyl labelled cRNA. Equal amounts of cRNA from all samples were mixed as reference. After conjugation of cRNA with fluorophores (Cy3/Cy5), each individual tumour cRNA was hybridised to a cDNA microarray together with reference cRNA, scanned and analysed. We show that our procedures for producing cDNA microarrays are reproducible. The concordance between duplicated spots and replicate hybridisation was found to be high. We have demonstrated that our cDNA microarrays are of a high technical quality. The majority of the cDNA microarrays had low local spot background levels. Despite the high background levels for some local spots, variation could be minimized by locally weighted scatter plot smooth normalisation (LOWESS), which we showed was also suitable for normalisation of cDNA microarrays with a limited number of probes.     

An evaluation of the performance of cDNA microarrays for detecting changes in global mRNA expression

The cDNA microarray is one technological approach that has the potential to accurately measure changes in global mRNA expression levels. We report an assessment of an optimized cDNA microarray platform to generate accurate, precise and reliable data consistent with the objective of using microarrays as an acquisition platform to populate gene expression databases. The study design consisted of two independent evaluations with 70 arrays from two different manufactured lots and used three human tissue sources as samples: placenta, brain and heart. Overall signal response was linear over three orders of magnitude and the sensitivity for any element was estimated to be 2 pg mRNA. The calculated coefficient of variation for differential expression for all non-differentiated elements was 12–14% across the entire signal range and did not vary with array batch or tissue source. The minimum detectable fold change for differential expression was 1.4. Accuracy, in terms of bias (observed minus expected differential expression ratio), was less than 1 part in 10 000 for all non-differentiated elements. The results presented in this report demonstrate the reproducible performance of the cDNA microarray technology platform and the methods provide a useful framework for evaluating other technologies that monitor changes in global mRNA expression.     

Paired versus two-group experimental design for rheological studies of vocal fold tissues

Vibratory function of the vocal folds is largely determined by the rheological properties or viscoelastic shear properties of the vocal fold lamina propria. To date, investigation of the sample size estimation and statistical experimental design for vocal fold rheological studies is nonexistent. The current work provides the closed-form sample size formulas for two major study designs (i.e. paired and two-group designs) in vocal fold research. Our results demonstrated that the paired design could greatly increase the statistical power compared to the two-group design. By comparing the variance of estimated treatment effect, this study also confirms that ignoring within-subject and within-vocal fold correlations during rheological data analysis will likely increase type I errors. Finally, viscoelastic shear properties of intact and scarred rabbit vocal fold lamina propria were measured and used to illustrate theoretical findings in a realistic scenario and project sample size requirement for future studies.     

Critical evaluation of the one- versus the two-channel model for the operation of the ATP synthase's F(o) motor

The mechanism of converting an electrochemical gradient of protons or Na(+) ions across the membrane into rotational torque by the F(o) motor of the ATP synthase has been described by a two-channel model or by a one-channel model. Experimental evidence obtained with the F(o) motor from the Propionigenium modestum ATP synthase is described which is in accordance with the one-channel model, but not with the two-channel model. This evidence includes the ATP-dependent occlusion of one (22)Na(+) per ATP synthase with a mutated Na(+)-impermeable a subunit or the Na(+)(in)/(22)Na(+)(out) exchange which is not affected by modifying part of the c subunit sites with dicyclohexylcarbodiimide.     

A Java-based tool for the design of classification microarrays

Background  

Classification microarrays are used for purposes such as identifying strains of bacteria and determining genetic relationships to understand the epidemiology of an infectious disease. For these cases, mixed microarrays, which are composed of DNA from more than one organism, are more effective than conventional microarrays composed of DNA from a single organism. Selection of probes is a key factor in designing successful mixed microarrays because redundant sequences are inefficient and limited representation of diversity can restrict application of the microarray. We have developed a Java-based software tool, called PLASMID, for use in selecting the minimum set of probe sequences needed to classify different groups of plasmids or bacteria.     

Statistical design of reverse dye microarrays

MOTIVATION: In cDNA microarray experiments all samples are labelled with either Cy3 dye or Cy5 dye. Certain genes exhibit dye bias-a tendency to bind more efficiently to one of the dyes. The common reference design avoids the problem of dye bias by running all arrays 'forward', so that the samples being compared are always labelled with the same dye. But comparison of samples labelled with different dyes is sometimes of interest. In these situations, it is necessary to run some arrays 'reverse'-with the dye labelling reversed-in order to correct for the dye bias. The design of these experiments will impact one's ability to identify genes that are differentially expressed in different tissues or conditions. We address the design issue of how many specimens are needed, how many forward and reverse labelled arrays to perform, and how to optimally assign Cy3 and Cy5 labels to the specimens. RESULTS: We consider three types of experiments for which some reverse labelling is needed: paired samples, samples from two predefined groups, and reference design data when comparison with the reference is of interest. We present simple probability models for the data, derive optimal estimators for relative gene expression, and compare the efficiency of the estimators for a range of designs. In each case, we present the optimal design and sample size formulas. We show that reverse labelling of individual arrays is generally not required.