Calculation of the minimum number of replicate spots required for detection of significant gene expression fold change in microarray experiments

MOTIVATION: We present statistical methods for determining the number of per gene replicate spots required in microarray experiments. The purpose of these methods is to obtain an estimate of the sampling variability present in microarray data, and to determine the number of replicate spots required to achieve a high probability of detecting a significant fold change in gene expression, while maintaining a low error rate. Our approach is based on data from control microarrays, and involves the use of standard statistical estimation techniques. RESULTS: After analyzing two experimental data sets containing control array data, we were able to determine the statistical power available for the detection of significant differential expression given differing levels of replication. The inclusion of replicate spots on microarrays not only allows more accurate estimation of the variability present in an experiment, but more importantly increases the probability of detecting genes undergoing significant fold changes in expression, while substantially decreasing the probability of observing fold changes due to chance rather than true differential expression.     

Gene expression profiling in mitochondrial disease: assessment of microarray accuracy by high-throughput Q-PCR

Mitochondrial diseases are a heterogeneous array of disorders with a complex etiology. Use of microarrays as a tool to investigate complex human disease is increasingly common, however, a principle drawback of microarrays is their limited dynamic range, due to the poor quantification of weak signals. Although it is generally understood that low-intensity microarray 'spots' may be unreliable, there exists little documentation of their accuracy. Quantitative PCR (Q-PCR) is frequently used to validate microarray data, yet few Q-PCR validation studies have focused on the accuracy of low-intensity microarray signals. Hence, we have used Q-PCR to systematically assess microarray accuracy as a function of signal strength in a mouse model of mitochondrial disease, the superoxide dismutase 2 (SOD2) nullizygous mouse. We have focused on a unique category of data--spots with only one weak signal in a two-dye comparative hybridization--and show that such 'high-low' signal intensities are common for differentially expressed genes. This category of differential expression may be more important in mitochondrial disease in which there are often mosaic expression patterns due to the idiosyncratic distribution of mutant mtDNA in heteroplasmic individuals. Using RNA from the SOD2 mouse, we found that when spotted cDNA microarray data are filtered for quality (low variance between many technical replicates) and spot intensity (above a negative control threshold in both channels), there is an excellent quantitative concordance with Q-PCR (R2 = 0.94). The accuracy of gene expression ratios from low-intensity spots (R2 = 0.27) and 'high-low' spots (R2 = 0.32) is considerably lower. Our results should serve as guidelines for microarray interpretation and the selection of genes for validation in mitochondrial disorders.     

Using human microarrays to identify differentially expressed genes associated with increased steroidogenesis in boars

Human microarrays are readily available, and it would be advantageous if they could be used to study gene expression in other species, such as pigs. The objectives of this research were to validate the use of human microarrays in the analysis of porcine gene expression, to assess the variability of the data generated, and to compare gene expression in boars with different levels of steroidogenesis. Cytochrome b5 (CYB5) expression was used to assess array detection sensitivity. Samples having high or low CYB5 RNA levels were hybridized to microarrays to determine if the known expression difference could be detected. Six hybridizations were conducted using human microarrays containing 3840 total spots representing 1718 characterized human ESTs. To analyze gene expression in boars with different levels of steroidogenesis, testis RNA from four boars with high levels of plasma estrone sulphate was hybridized to testis RNA from four boars with lower levels. Eight microarray hybridizations were conducted including fluor-flips. Self-self hybridizations were also conducted to assess the variability of array experiments. The Cy5 and Cy3 intensity values for each array were normalized using a locally weighted linear regression (LOESS). Statistical significance was assessed using a Student's t-test followed by the Benjamini and Hochberg multiple testing correction procedure. Quantitative real-time PCR (Q-RT-PCR) was used to verify select gene expression differences. The results show that CYB5 was significantly overexpressed in the high CYB5 sample by 1.8 fold (P < 0.05), verifying the known expression difference. The average log2 ratio of the majority of genes (1643) falls within one standard deviation of the mean, indicating the data were reproducible. In the high versus low steroidogenesis experiment, seven genes were significantly overexpressed in the high group (P < 0.05). Quantitative real-time PCR was used to validate five genes with the highest fold change, and the results corroborated those found by the microarray experiments. The results of the self-self hybridizations showed that no genes were significantly differentially expressed following the application of the Benjamini and Hochberg multiple testing correction procedure. The results presented in this report show that human arrays can be used for gene expression analysis in pigs.     

Using Human Microarrays to Identify Differentially Expressed Genes Associated with Increased Steroidogenesis in Boars

Human microarrays are readily available, and it would be advantageous if they could be used to study gene expression in other species, such as pigs. The objectives of this research were to validate the use of human microarrays in the analysis of porcine gene expression, to assess the variability of the data generated, and to compare gene expression in boars with different levels of steroidogenesis. Cytochrome b5 (CYB5) expression was used to assess array detection sensitivity. Samples having high or low CYB5 RNA levels were hybridized to microarrays to determine if the known expression difference could be detected. Six hybridizations were conducted using human microarrays containing 3840 total spots representing 1718 characterized human ESTs. To analyze gene expression in boars with different levels of steroidogenesis, testis RNA from four boars with high levels of plasma estrone sulphate was hybridized to testis RNA from four boars with lower levels. Eight microarray hybridizations were conducted including fluor-flips. Self-self hybridizations were also conducted to assess the variability of array experiments. The Cy5 and Cy3 intensity values for each array were normalized using a locally weighted linear regression (LOESS). Statistical significance was assessed using a Student's t-test followed by the Benjamini and Hochberg multiple testing correction procedure. Quantitative real-time PCR (Q-RT-PCR) was used to verify select gene expression differences. The results show that CYB5 was significantly overexpressed in the high CYB5 sample by 1.8 fold (P < 0.05), verifying the known expression difference. The average log2 ratio of the majority of genes (1643) falls within one standard deviation of the mean, indicating the data were reproducible. In the high versus low steroidogenesis experiment, seven genes were significantly overexpressed in the high group (P < 0.05). Quantitative real-time PCR was used to validate five genes with the highest fold change, and the results corroborated those found by the microarray experiments. The results of the self-self hybridizations showed that no genes were significantly differentially expressed following the application of the Benjamini and Hochberg multiple testing correction procedure. The results presented in this report show that human arrays can be used for gene expression analysis in pigs.     

Monitoring of cDNA microarray with common primer target and hybridization specificity with selected targets

Academic researchers are increasingly producing and using cDNA microarrays. Their quality and hybridization specificity are crucial in determining whether the generated data are accurate and interpretable. Here, we describe two methods of monitoring microarray production, the sustainability of DNA attachment, and the specificity of hybridization. The first method consists of labeling an oligonucleotide, which is one of the primers used to amplify all cDNA probes on the array (except for beta-actin and GAPDH) with fluorescent dye and hybridize it to the cDNA microarray. Attachment of the cDNAs on the array after the hybridization procedure was monitored by visualizing fluorescent signals from the spots on the array. In the second method, two selected DNA targets, beta-actin and GAPDH, were labeled with fluorescent dye to hybridize to the cDNA array. Hence, hybridization specificity was demonstrated by obtaining fluorescent signals solely from the genes corresponding to the target.     

MADGE: scalable distributed data management software for cDNA microarrays

MOTIVATION: The human genome project and the development of new high-throughput technologies have created unparalleled opportunities to study the mechanism of diseases, monitor the disease progression and evaluate effective therapies. Gene expression profiling is a critical tool to accomplish these goals. The use of nucleic acid microarrays to assess the gene expression of thousands of genes simultaneously has seen phenomenal growth over the past five years. Although commercial sources of microarrays exist, investigators wanting more flexibility in the genes represented on the array will turn to in-house production. The creation and use of cDNA microarrays is a complicated process that generates an enormous amount of information. Effective data management of this information is essential to efficiently access, analyze, troubleshoot and evaluate the microarray experiments. RESULTS: We have developed a distributable software package designed to track and store the various pieces of data generated by a cDNA microarray facility. This includes the clone collection storage data, annotation data, workflow queues, microarray data, data repositories, sample submission information, and project/investigator information. This application was designed using a 3-tier client server model. The data access layer (1st tier) contains the relational database system tuned to support a large number of transactions. The data services layer (2nd tier) is a distributed COM server with full database transaction support. The application layer (3rd tier) is an internet based user interface that contains both client and server side code for dynamic interactions with the user. AVAILABILITY: This software is freely available to academic institutions and non-profit organizations at http://www.genomics.mcg.edu/niddkbtc.     

TECHNICAL ADVANCES: A microarray for large-scale genomic and transcriptional analyses of the zebra finch (Taeniopygia guttata) and other passerines

The microarray technology has revolutionized biological research in the last decade. By monitoring the expression of many genes simultaneously, microarrays can elucidate gene function, as well as scan entire genomes for candidate genes encoding complex traits. However, because of high costs of sequencing and design, microarrays have largely been restricted to a few model species. Cross-species microarray (CSM) analyses, where microarrays are used for other species than the one they were designed for, have had varied success. We have conducted a CSM analysis by hybridizing genomic DNA from the common whitethroat (Sylvia communis) on a newly developed Affymetrix array designed for the zebra finch (Taeniopygia guttata), the Lund-zf array. The results indicate a very high potential for the zebra finch array to act as a CSM utility in other passerine birds. When hybridizing zebra finch genomic DNA, 98% of the gene representatives had higher signal intensities than the background cut-off, and for the common whitethroat, we found the equivalent proportion to be as high as 96%. This was surprising given the fact that finches and warblers diverged 25-50 million years ago, but may be explained by a relatively low sequence divergence between passerines (89-93%). Passerine birds are widely used in studies of ecology and evolution, and a zebra finch array that can be used for many species may have a large impact on future research directions.     

Nonparametric testing for DNA copy number induced differential mRNA gene expression

Summary .  The central dogma of molecular biology relates DNA with mRNA. Array CGH measures DNA copy number and gene expression microarrays measure the amount of mRNA. Methods that integrate data from these two platforms may uncover meaningful biological relationships that further our understanding of cancer. We develop nonparametric tests for the detection of copy number induced differential gene expression. The tests incorporate the uncertainty of the calling of genomic aberrations. The test is preceded by a "tuning algorithm" that discards certain genes to improve the overall power of the false discovery rate selection procedure. Moreover, the test statistics are "shrunken" to borrow information across neighboring genes that share the same array CGH signature. For each gene we also estimate its effect, its amount of differential expression due to copy number changes, and calculate the coefficient of determination. The method is illustrated on breast cancer data, in which it confirms previously reported findings, now with a more profound statistical underpinning.     

Pharmacogenomics of the cystic fibrosis transmembrane conductance regulator (CFTR) and the cystic fibrosis drug CPX using genome microarray analysis

BACKGROUND: Cystic fibrosis (CF) is the most common lethal recessive disease affecting children in the U.S. and Europe. For this reason, a number of ongoing attempts are being made to treat the disease either by gene therapy or pharmacotherapy. Several phase 1 gene therapy trials have been completed, and a phase 2 clinical trial with the xanthine drug CPX is in progress. The protein coded by the principal CFTR mutation, DeltaF508-CFTR, fails to traffic efficiently from the endoplasmic reticulum to the plasma membrane, and is the pathogenic basis for the missing cAMP-activated plasma membrane chloride channel. CPX acts by binding to the mutant DeltaF508-CFTR and correcting the trafficking deficit. CPX also activates mutant CFTR channels. The comparative genomics of wild-type and mutant CFTR has not previously been studied. However, we have hypothesized that the gene expression patterns of human cells expressing mutant or wild-type CFTR might differ, and that a drug such as CPX might convert the mutant gene expression pattern into one more characteristic of wild-type CFTR. To the extent that this is true, a pharmacogenomic profile for such corrective drugs might be deduced that could simplify the process of drug discovery for CF. MATERIALS AND METHODS: To test this hypothesis we used cDNA microarrays to study global gene expression in human cells permanently transfected with either wild-type or mutant CFTR. We also tested the effects of CPX on global gene expression when incubated with cells expressing either mutant or wild-type CFTR. RESULTS: Wild-type and mutant DeltaF508-CFTR induce distinct and differential changes in cDNA microarrays, significantly affecting up to 5% of the total genes in the array. CPX also induces substantial mutation-dependent and -independent changes in gene expression. Some of these changes involve movement of gene expression in mutant cells in a direction resembling expression in wild-type cells. CONCLUSIONS: These data clearly demonstrate that cDNA array analysis of cystic fibrosis cells can yield useful pharmacogenomic information with significant relevance to both gene and pharmacological therapy. We suggest that this approach may provide a paradigm for genome-based surrogate endpoint testing of CF therapeutics prior to human administration.     

Application of microarray technology in primate behavioral neuroscience research

Gene expression profiling of brain tissue samples applied to DNA microarrays promises to provide novel insights into the neurobiological bases of primate behavior. The strength of the microarray technology lies in the ability to simultaneously measure the expression levels of all genes in defined brain regions that are known to mediate behavior. The application of microarrays presents, however, various limitations and challenges for primate neuroscience research. Low RNA abundance, modest changes in gene expression, heterogeneous distribution of mRNA among cell subpopulations, and individual differences in behavior all mandate great care in the collection, processing, and analysis of brain tissue. A unique problem for nonhuman primate research is the limited availability of species-specific arrays. Arrays designed for humans are often used, but expression level differences are inevitably confounded by gene sequence differences in all cross-species array applications. Tools to deal with this problem are currently being developed. Here we review these methodological issues, and provide examples from our experiences using human arrays to examine brain tissue samples from squirrel monkeys. Until species-specific microarrays become more widely available, great caution must be taken in the assessment and interpretation of microarray data from nonhuman primates. Nevertheless, the application of human microarrays in nonhuman primate neuroscience research recovers useful information from thousands of genes, and represents an important new strategy for understanding the molecular complexity of behavior and mental health.     

Gene expression and biochemical analysis of cheese-ripening yeasts: focus on catabolism of L-methionine, lactate, and lactose

DNA microarrays of 86 genes from the yeasts Debaryomyces hansenii, Kluyveromyces marxianus, and Yarrowia lipolytica were developed to determine which genes were expressed in a medium mimicking a cheese-ripening environment. These genes were selected for potential involvement in lactose/lactate catabolism and the biosynthesis of sulfur-flavored compounds. Hybridization conditions to follow specifically the expression of homologous genes belonging to different species were set up. The microarray was first validated on pure cultures of each yeast; no interspecies cross-hybridization was observed. Expression patterns of targeted genes were studied in pure cultures of each yeast, as well as in coculture, and compared to biochemical data. As expected, a high expression of the LAC genes of K. marxianus was observed. This is a yeast that efficiently degrades lactose. Several lactate dehydrogenase-encoding genes were also expressed essentially in D. hansenii and K. marxianus, which are two efficient deacidifying yeasts in cheese ripening. A set of genes possibly involved in l-methionine catabolism was also used on the array. Y. lipolytica, which efficiently assimilates l-methionine, also exhibited a high expression of the Saccharomyces cerevisiae orthologs BAT2 and ARO8, which are involved in the l-methionine degradation pathway. Our data provide the first evidence that the use of a multispecies microarray could be a powerful tool to investigate targeted metabolism and possible metabolic interactions between species within microbial cocultures.     

M<Superscript>3</Superscript>G: Maximum Margin Microarray Gridding

Background  

Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding.     

Selection and validation of normalization methods for c-DNA microarrays using within-array replications

MOTIVATION: Normalization of microarray data is essential for multiple-array analyses. Several normalization protocols have been proposed based on different biological or statistical assumptions. A fundamental problem arises whether they have effectively normalized arrays. In addition, for a given array, the question arises how to choose a method to most effectively normalize the microarray data. RESULTS: We propose several techniques to compare the effectiveness of different normalization methods. We approach the problem by constructing statistics to test whether there are any systematic biases in the expression profiles among duplicated spots within an array. The test statistics involve estimating the genewise variances. This is accomplished by using several novel methods, including empirical Bayes methods for moderating the genewise variances and the smoothing methods for aggregating variance information. P-values are estimated based on a normal or chi approximation. With estimated P-values, we can choose a most appropriate method to normalize a specific array and assess the extent to which the systematic biases due to the variations of experimental conditions have been removed. The effectiveness and validity of the proposed methods are convincingly illustrated by a carefully designed simulation study. The method is further illustrated by an application to human placenta cDNAs comprising a large number of clones with replications, a customized microarray experiment carrying just a few hundred genes on the study of the molecular roles of Interferons on tumor, and the Agilent microarrays carrying tens of thousands of total RNA samples in the MAQC project on the study of reproducibility, sensitivity and specificity of the data. AVAILABILITY: Code to implement the method in the statistical package R is available from the authors.     

Use of within-array replicate spots for assessing differential expression in microarray experiments

MOTIVATION: Spotted arrays are often printed with probes in duplicate or triplicate, but current methods for assessing differential expression are not able to make full use of the resulting information. The usual practice is to average the duplicate or triplicate results for each probe before assessing differential expression. This results in the loss of valuable information about genewise variability. RESULTS: A method is proposed for extracting more information from within-array replicate spots in microarray experiments by estimating the strength of the correlation between them. The method involves fitting separate linear models to the expression data for each gene but with a common value for the between-replicate correlation. The method greatly improves the precision with which the genewise variances are estimated and thereby improves inference methods designed to identify differentially expressed genes. The method may be combined with empirical Bayes methods for moderating the genewise variances between genes. The method is validated using data from a microarray experiment involving calibration and ratio control spots in conjunction with spiked-in RNA. Comparing results for calibration and ratio control spots shows that the common correlation method results in substantially better discrimination of differentially expressed genes from those which are not. The spike-in experiment also confirms that the results may be further improved by empirical Bayes smoothing of the variances when the sample size is small. AVAILABILITY: The methodology is implemented in the limma software package for R, available from the CRAN repository http://www.r-project.org