Normalization of boutique two-color microarrays with a high proportion of differentially expressed probes

Normalization is critical for removing systematic variation from microarray data. For two-color microarray platforms, intensity-dependent lowess normalization is commonly used to correct relative gene expression values for biases. Here we outline a normalization method for use when the assumptions of lowess normalization fail. Specifically, this can occur when specialized boutique arrays are constructed that contain a subset of genes selected to test particular biological functions.     

Functional analysis and comparative genomics of expressed sequence tags from the lycophyte Selaginella moellendorffii

Background

DNA microarrays are widely used in gene expression analyses. To increase throughput and minimize costs without reducing gene expression data obtained, we investigated whether four mRNA samples can be analyzed simultaneously by applying four different fluorescent dyes.

Results

Following tests for cross-talk of fluorescence signals, Alexa 488, Alexa 594, Cyanine 3 and Cyanine 5 were selected for hybridizations. For self-hybridizations, a single RNA sample was labelled with all dyes and hybridized on commercial cDNA arrays or on in-house spotted oligonucleotide arrays. Correlation coefficients for all combinations of dyes were above 0.9 on the cDNA array. On the oligonucleotide array they were above 0.8, except combinations with Alexa 488, which were approximately 0.5. Standard deviation of expression differences for replicate spots were similar on the cDNA array for all dye combinations, but on the oligonucleotide array combinations with Alexa 488 showed a higher variation.

Conclusion

In conclusion, the four dyes can be used simultaneously for gene expression experiments on the tested cDNA array, but only three dyes can be used on the tested oligonucleotide array. This was confirmed by hybridizations of control with test samples, as all combinations returned similar numbers of differentially expressed genes with comparable effects on gene expression.     

Removing unwanted variation in a differential methylation analysis of Illumina HumanMethylation450 array data

Due to their relatively low-cost per sample and broad, gene-centric coverage of CpGs across the human genome, Illumina''s 450k arrays are widely used in large scale differential methylation studies. However, by their very nature, large studies are particularly susceptible to the effects of unwanted variation. The effects of unwanted variation have been extensively documented in gene expression array studies and numerous methods have been developed to mitigate these effects. However, there has been much less research focused on the appropriate methodology to use for accounting for unwanted variation in methylation array studies. Here we present a novel 2-stage approach using RUV-inverse in a differential methylation analysis of 450k data and show that it outperforms existing methods.     

Theoretical and experimental comparisons of gene expression indexes for oligonucleotide arrays

MOTIVATION: Oligonucleotide expression arrays exhibit systematic and reproducible variation produced by the multiple distinct probes used to represent a gene. Recently, a gene expression index has been proposed that explicitly models probe effects, and provides improved fits of hybridization intensity for arrays containing perfect match (PM) and mismatch (MM) probe pairs. RESULTS: Here we use a combination of analytical arguments and empirical data to show directly that the estimates provided by model-based expression indexes are superior to those provided by commercial software. The improvement is greatest for genes in which probe effects vary substantially, and modeling the PM and MM intensities separately is superior to using the PM-MM differences. To empirically compare expression indexes, we designed a mixing experiment involving three groups of human fibroblast cells (serum starved, serum stimulated, and a 50:50 mixture of starved/stimulated), with six replicate HuGeneFL arrays in each group. Careful spiking of control genes provides evidence that 88-98% of the genes on the array are detectably transcribed, and that the model-based estimates can accurately detect the presence versus absence of a gene. The use of extensive replication from single RNA sources enables exploration of the technical variability of the array.     

Receiver operating characteristic analysis: a general tool for DNA array data filtration and performance estimation

A critical step for DNA array analysis is data filtration, which can reduce thousands of detected signals to limited sets of genes. Commonly accepted rules for such filtration are still absent. We present a rational approach, based on thresholding of intensities with cutoff levels that are estimated by receiver operating characteristic (ROC) analysis. The technique compares test results with known distributions of positive and negative signals. We apply the method to Atlas cDNA arrays, GeneFilters, and Affymetrix GeneChip. ROC analysis demonstrates similarities in the distribution of false and true positive data for these different systems. We illustrate the estimation of an optimal cutoff level for intensity-based filtration, providing the highest ratio of true to false signals. For GeneChip arrays, we derived filtration thresholds consistent with the reported data based on replicate hybridizations. Intensity-based filtration optimized with ROC combined with other types of filtration (for example, based on significances of differences and/or ratios), should improve DNA array analysis. ROC methodology is also demonstrated for comparison of the performance of different types of arrays, imagers, and analysis software.     

Transcriptional signatures of normal human mammary epithelial cells in response to benzo[a]pyrene exposure: a comparison of three microarray platforms

Microarrays are used to study gene expression in a variety of biological systems. A number of different platforms have been developed, but few studies exist that have directly compared the performance of one platform with another. The goal of this study was to determine array variation by analyzing the same RNA samples with three different array platforms. Using gene expression responses to benzo[a]pyrene exposure in normal human mammary epithelial cells (NHMECs), we compared the results of gene expression profiling using three microarray platforms: photolithographic oligonucleotide arrays (Affymetrix), spotted oligonucleotide arrays (Amersham), and spotted cDNA arrays (NCI). While most previous reports comparing microarrays have analyzed pre-existing data from different platforms, this comparison study used the same sample assayed on all three platforms, allowing for analysis of variation from each array platform. In general, poor correlation was found with corresponding measurements from each platform. Each platform yielded different gene expression profiles, suggesting that while microarray analysis is a useful discovery tool, further validation is needed to extrapolate results for broad use of the data. Also, microarray variability needs to be taken into consideration, not only in the data analysis but also in specific probe selection for each array type.     

Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation

There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is often used to force the distribution of the intensity log ratios to have a median of zero for each slide. However, such global normalization approaches are not adequate in situations where dye biases can depend on spot overall intensity and/or spatial location within the array. This article proposes normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments. The selection of appropriate controls for normalization is discussed and a novel set of controls (microarray sample pool, MSP) is introduced to aid in intensity-dependent normalization. Lastly, to allow for comparisons of expression levels across slides, a robust method based on maximum likelihood estimation is proposed to adjust for scale differences among slides.     

A quality-controlled microarray method for gene expression profiling

Gene expression profiling on microarrays is widely used to measure the expression of large numbers of genes in a single experiment. Because of the high cost of this method, feasible numbers of replicates are limited, thus impairing the power of statistical analysis. As a step toward reducing technically induced variation, we developed a procedure of sample preparation and analysis that minimizes the number of sample manipulation steps, introduces quality control before array hybridization, and allows recovery of the prepared mRNA for independent validation of results. Sample preparation is based on mRNA separation using oligo(dT) magnetic beads, which are subsequently used for first-strand cDNA synthesis on the beads. cDNA covalently bound to the magnetic beads is used as template for second-strand cDNA synthesis, leaving the intact mRNA in solution for further analysis. The quality of the synthesized cDNA can be assessed by quantitative polymerase chain reaction using 3'- and 5'-specific primer pairs for housekeeping genes such as glyceraldehyde-3-phosphate dehydrogenase. Second-strand cDNA is chemically labeled with fluorescent dyes to avoid dye bias in enzymatic labeling reactions. After hybridization of two differently labeled samples to microarray slides, arrays are scanned and images analyzed automatically with high reproducibility. Quantile-normalized data from five biological replica display a coefficient of variation 45% for 90% of profiled genes, allowing detection of twofold changes with false positive and false negative rates of 10% each. We demonstrate successful application of the procedure for expression profiling in plant leaf tissue. However, the method could be easily adapted for samples from animal including human or from microbial origin.     

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.     

Normalization of single-channel DNA array data by principal component analysis

MOTIVATION: Detailed comparison and analysis of the output of DNA gene expression arrays from multiple samples require global normalization of the measured individual gene intensities from the different hybridizations. This is needed for accounting for variations in array preparation and sample hybridization conditions. RESULTS: Here, we present a simple, robust and accurate procedure for the global normalization of datasets generated with single-channel DNA arrays based on principal component analysis. The procedure makes minimal assumptions about the data and performs well in cases where other standard procedures produced biased estimates. It is also insensitive to data transformation, filtering (thresholding) and pre-screening.     

A comparison of cDNA, oligonucleotide, and Affymetrix GeneChip gene expression microarray platforms.

We have conducted a study to compare the variability in measured gene expression levels associated with three types of microarray platforms. Total RNA samples were obtained from liver tissue of four male mice, two each from inbred strains A/J and C57BL/6J. The same four samples were assayed on Affymetrix Mouse Genome Expression Set 430 GeneChips (MOE430A and MOE430B), spotted cDNA microarrays, and spotted oligonucleotide microarrays using eight arrays of each type. Variances associated with measurement error were observed to be comparable across all microarray platforms. The MOE430A GeneChips and cDNA arrays had higher precision across technical replicates than the MOE430B GeneChips and oligonucleotide arrays. The Affymetrix platform showed the greatest range in the magnitude of expression levels followed by the oligonucleotide arrays. We observed good concordance in both estimated expression level and statistical significance of common genes between the Affymetrix MOE430A GeneChip and the oligonucleotide arrays. Despite their apparently high precision, cDNA arrays showed poor concordance with other platforms.     

Pre-processing Agilent microarray data

Background  

Pre-processing methods for two-sample long oligonucleotide arrays, specifically the Agilent technology, have not been extensively studied. The goal of this study is to quantify some of the sources of error that affect measurement of expression using Agilent arrays and to compare Agilent's Feature Extraction software with pre-processing methods that have become the standard for normalization of cDNA arrays. These include log transformation followed by loess normalization with or without background subtraction and often a between array scale normalization procedure. The larger goal is to define best study design and pre-processing practices for Agilent arrays, and we offer some suggestions.     

Gene expression changes associated with cytotoxicity identified using cDNA arrays

In order to investigate gene expression changes associated with cytotoxicity, we used cDNA arrays to monitor the expression of over 5,000 genes in response to toxic stress in the HepG2 liver cell line. Cells were treated with cytotoxic doses of acetaminophen, caffeine or thioacetamide for nine time points ranging from 1 to 24 h. Samples of mRNA from each time point were used to prepare radiolabeled cDNA, which was hybridized to nylon-membrane-based cDNA arrays. High-stringency washes were applied to reduce cross-hybridization. Analysis of spot intensities revealed that each compound led to approximately 150-250 gene expression changes that were sustained over at least three adjacent time points. The affected genes could be classified into clusters based on their temporal patterns of differential expression. A common set of 44 genes showed similar expression changes in response to all three compounds. Of these changes, 90% could be confirmed by quantitative RT-PCR analysis. The results indicate that detailed array-based time-course studies, coupled with a sensitive and highly specific confirmation assay, provide a powerful means of identifying cytotoxicity-associated gene expression changes. Electronic Publication     

Prokaryotic RNA preparation methods useful for high density array analysis: comparison of two approaches

High density oligonucleotide arrays have been used extensively for expression studies of eukaryotic organisms. We have designed a prokaryotic high density oligonucleotide array using the complete Escherichia coli genome sequence to monitor expression levels of all genes and intergenic regions in the genome. Because previously described methods for preparing labeled target nucleic acids are not useful for prokaryotic cell analysis using such arrays, a mRNA enrichment and direct labeling protocol was developed together with a cDNA synthesis protocol. The reproducibility of each labeling method was determined using high density oligonucleotide probe arrays as a read-out methodology and the expression results from direct labeling were compared to the expression results from the cDNA synthesis. About 50% of all annotated E.coli open reading frames are observed to be transcribed, as measured by both protocols, when the cells were grown in rich LB medium. Each labeling method individually showed a high degree of concordance in replica experiments (95 and 99%, respectively), but when each sample preparation method was compared to the other, ~32% of the genes observed to be expressed were discordant. However, both labeling methods can detect the same relative gene expression changes when RNA from IPTG-induced cells was labeled and compared to RNA from uninduced E.coli cells.     

Genome-wide expression profiling in Escherichia coli K-12.

We have established high resolution methods for global monitoring of gene expression in Escherichia coli. Hybridization of radiolabeled cDNA to spot blots on nylon membranes was compared to hybridization of fluorescently-labeled cDNA to glass microarrays for efficiency and reproducibility. A complete set of PCR primers was created for all 4290 annotated open reading frames (ORFs) from the complete genome sequence of E.coli K-12 (MG1655). Glass- and nylon-based arrays of PCR products were prepared and used to assess global changes in gene expression. Full-length coding sequences for array printing were generated by two-step PCR amplification. In this study we measured changes in RNA levels after exposure to heat shock and following treatment with isopropyl-beta-D-thiogalactopyranoside (IPTG). Both radioactive and fluorescence-based methods showed comparable results. Treatment with IPTG resulted in high level induction of the lacZYA and melAB operons. Following heat shock treatment 119 genes were shown to have significantly altered expression levels, including 35 previously uncharacterized ORFs and most genes of the heat shock stimulon. Analysis of spot intensities from hybridization to replicate arrays identified sets of genes with signals consistently above background suggesting that at least 25% of genes were expressed at detectable levels during growth in rich media.     

Genetic Association Studies of Copy-Number Variation: Should Assignment of Copy Number States Precede Testing?

Recently, structural variation in the genome has been implicated in many complex diseases. Using genomewide single nucleotide polymorphism (SNP) arrays, researchers are able to investigate the impact not only of SNP variation, but also of copy-number variants (CNVs) on the phenotype. The most common analytic approach involves estimating, at the level of the individual genome, the underlying number of copies present at each location. Once this is completed, tests are performed to determine the association between copy number state and phenotype. An alternative approach is to carry out association testing first, between phenotype and raw intensities from the SNP array at the level of the individual marker, and then aggregate neighboring test results to identify CNVs associated with the phenotype. Here, we explore the strengths and weaknesses of these two approaches using both simulations and real data from a pharmacogenomic study of the chemotherapeutic agent gemcitabine. Our results indicate that pooled marker-level testing is capable of offering a dramatic increase in power (> 12-fold) over CNV-level testing, particularly for small CNVs. However, CNV-level testing is superior when CNVs are large and rare; understanding these tradeoffs is an important consideration in conducting association studies of structural variation.