Microarray standard data set and figures of merit for comparing data processing methods and experiment designs

MOTIVATION: There is a very large and growing level of effort toward improving the platforms, experiment designs, and data analysis methods for microarray expression profiling. Along with a growing richness in the approaches there is a growing confusion among most scientists as to how to make objective comparisons and choices between them for different applications. There is a need for a standard framework for the microarray community to compare and improve analytical and statistical methods. RESULTS: We report on a microarray data set comprising 204 in-situ synthesized oligonucleotide arrays, each hybridized with two-color cDNA samples derived from 20 different human tissues and cell lines. Design of the approximately 24 000 60mer oligonucleotides that report approximately 2500 known genes on the arrays, and design of the hybridization experiments, were carried out in a way that supports the performance assessment of alternative data processing approaches and of alternative experiment and array designs. We also propose standard figures of merit for success in detecting individual differential expression changes or expression levels, and for detecting similarities and differences in expression patterns across genes and experiments. We expect this data set and the proposed figures of merit will provide a standard framework for much of the microarray community to compare and improve many analytical and statistical methods relevant to microarray data analysis, including image processing, normalization, error modeling, combining of multiple reporters per gene, use of replicate experiments, and sample referencing schemes in measurements based on expression change. AVAILABILITY/SUPPLEMENTARY INFORMATION: Expression data and supplementary information are available at http://www.rii.com/publications/2003/HE_SDS.htm     

Selection and validation of microarray candidate genes from subregions and subnuclei of the brain

DNA array technology now allows an enormous amount of expression data to be obtained. For large-scale gene profiling enterprises, this is of course welcome. However, the scientist interested in follow-up studies of a handful of differentially expressed genes may find it hard to sift through the vast datasets to pinpoint genes with the most desirable and reliable behaviors. Here, we present the methodology we have employed to discover genes differentially expressed in the adult mouse brain. We first used Affymetrix microarrays to compare gene expression from five different brain regions: the amygdala, cerebellum, hippocampus, olfactory bulb, and periaqueductal gray. Second, we identified genes differentially expressed within three distinct amygdala subnuclei. In this case, the tissue was microdissected by laser-capture to minimize contamination from adjacent subnuclei, and extracted RNA was subjected to three rounds of linear amplification prior to hybridization to the microarrays. To select candidate genes, we developed a custom algorithm to identify those genes with the most robust changes in expression across different replicate samples. Confirmation of expression patterns with in situ hybridization uncovered further criteria to consider in the selection process.     

DNA microarray technology: a new tool for the epidemiological typing of bacterial pathogens?

Genomic hybridization on whole genome arrays detects the presence or absence of similar DNA regions in sufficiently related microorganisms, allowing genome-wide comparison of their genetic contents. A whole genome array is based on a sequenced bacterial isolate, and is a collection of DNA probes fixed on a solid support. In a single hybridization experiment, the absence/presence status of all genes of the sequenced microbe in the queried isolate can be examined. The objective of this minireview is to summarize the past usage of DNA microarray technology for microbial strain characterizations, and to estimate its future utilization in epidemiological studies and molecular typing of bacterial pathogens. The studies reviewed here confirm the usefulness of microarray technology for the detection of genetic polymorphisms. However, the construction or purchase of DNA microarrays and the performance of strain to strain hybridization experiments are still prohibitively expensive for routine application. Future use of arrays in epidemiology is likely to depend on the development of more cost-effective protocols, more robust and simplified formats, and the adequate evaluation of their performance (efficacy) and convenience (efficiency) compared with other genotyping methods. It seems more likely that a more focused assay, concentrating on genomic regions of variability previously detected by genome-wide microarrays, will find broad application in routine bacterial epidemiology.     

Ranking: a closer look on globalisation methods for normalisation of gene expression arrays

Data from gene expression arrays are influenced by many experimental parameters that lead to variations not simply accessible by standard quantification methods. To compare measurements from gene expression array experiments, quantitative data are commonly normalised using reference genes or global normalisation methods based on mean or median values. These methods are based on the assumption that (i) selected reference genes are expressed at a standard level in all experiments or (ii) that mean or median signal of expression will give a quantitative reference for each individual experiment. We introduce here a new ranking diagram, with which we can show how the different normalisation methods compare, and how they are influenced by variations in measurements (noise) that occur in every experiment. Furthermore, we show that an upper trimmed mean provides a simple and robust method for normalisation of larger sets of experiments by comparative analysis.     

Development of a toxicological gene array and quantitative assessment of this technology

High-density arrays of DNA bound to solid substrates offer a powerful approach to identifying changes in gene expression in response to toxicants. While DNA arrays have been used to explore qualitative changes in gene regulation, less attention has focused on the quantitative aspects of this technology. Arrays containing expressed sequence tags for xenobiotic metabolizing enzymes, proteins associated with glutathione regulation, DNA repair enzymes, heat shock proteins, and housekeeping genes were used to examine gene expression in response to beta-naphthoflavone (beta-NF). Upregulation of cytochrome P4501a1 (Cyp1a1) and 1a2 in mouse liver was maximal 8 h after beta-NF administration. Significant upregulation of Cyp1a2 was noted at beta-NF doses as low as 0.62 and 1.2 mg/kg when gene expression was measured by microarray or Northern blotting, respectively. Maximal Cyp1a2 induction is 5-fold by Northern analysis and 10-fold by microarray. Induction of Cyp1a1 was 15- and 20-fold by Northern and microarray analysis, respectively. The coefficient of variation for spot to spot and slide to slide comparisons was <15%; this variability was smaller than interanimal variability (18-60%). Comparison of mRNA expression in control animals indicated that there are differences in labeling/detection associated with Cy3/Cy5 dyes; accordingly, experiments must include methods for establishing baseline signals for all genes. We conclude that the dynamic range and sensitivity of DNA microarrays on glass slides is comparable to Northern blotting analysis and that variability of the data introduced during spotting and hybridization is less than the interanimal variability.     

Striptease on glass: validation of an improved stripping procedure for in situ microarrays

Microarrays have rapidly become an indispensable tool for gene analysis. Microarray experiments can be cost prohibitive, however, largely due to the price of the arrays themselves. Whilst different methods for stripping filter arrays on membranes have been established, only very few protocols are published for thermal and chemical stripping of microarrays on glass. Most of these protocols for stripping microarrays on glass were developed in combination with specific surface chemistry and different coatings for covalently immobilizing presynthesized DNA in a deposition process. We have developed a method for stripping commercial in situ microarrays using a multi-step procedure. We present a method that uses mild chemical degradation complemented by enzymatic treatment. We took advantage of the differences in biochemical properties of covalently linked DNA oligonucleotides on in situ synthesized microarrays and the antisense cRNA hybridization probes. The success of stripping protocols for microarrays on glass was critically dependent on the type of arrays, the nature of sample used for hybridization, as well as hybridization and washing conditions. The protocol employs alkali hydrolysis of the cRNA, several enzymatic degradation steps using RNAses and Proteinase K, combined with appropriate washing steps. Stripped arrays were rehybridized using the same protocols as for new microarrays. The stripping method was validated with microarrays from different suppliers and rehybridization of stripped in situ arrays yielded comparable results to hybridizations done on unused, new arrays with no significant loss in precision or accuracy. We show that stripping of commercial in situ arrays is feasible and that reuse of stripped arrays gave similar results compared to unused ones. This was true even for biological samples that show only slight differences in their expression profiles. Our analyses indicate that the stripping procedure does not significantly influence data quality derived from post-primary hybridizations. The method is robust, easy to perform, inexpensive, and results after reuse are of comparable accuracy to new arrays.     

A renewed approach to the nonparametric analysis of replicated microarray experiments

DNA-microarrays find broad employment in biochemical research. This technology allows the monitoring of the expression levels of thousands of genes at the same time. Often, the goal of a microarray study is to find differentially expressed genes in two different types of tissue, for example normal and cancerous. Multiple hypothesis testing is a useful statistical tool for such studies. One approach using multiple hypothesis testing is nonparametric analysis for replicated microarray experiments. In this paper we present an improved version of this method. We also show how p-values are calculated for all significant genes detected with this testing procedure. All algorithms were implemented in an R-package, and instructions on it's use are included. The package can be downloaded at http://www.statistik.unidortmund.de/de/content/einrichtungen/lehrstuehle/personen/jung.html     

Current issues for DNA microarrays: platform comparison, double linear amplification, and universal RNA reference

DNA microarray technology has been widely used to simultaneously determine the expression levels of thousands of genes. A variety of approaches have been used, both in the implementation of this technology and in the analysis of the large amount of expression data. However, several practical issues still have not been resolved in a satisfactory manner, and among the most critical is the lack of agreement in the results obtained in different array platforms. In this study, we present a comparison of several microarray platforms [Affymetrix oligonucleotide arrays, custom complementary DNA (cDNA) arrays, and custom oligo arrays printed with oligonucleotides from three different sources] as well as analysis of various methods used for microarray target preparation and the reference design. The results indicate that the pairwise correlations of expression levels between platforms are relative low overall but that the log ratios of the highly expressed genes are strongly correlated, especially between Affymetrix and cDNA arrays. The microarray measurements were compared with quantitative real-time-polymerase chain reaction (QRT-PCR) results for 23 genes, and the varying degrees of agreement for each platform were characterized. We have also developed and tested a double amplification method which allows the use of smaller amounts of starting material. The added round of amplification produced reproducible results as compared to the arrays hybridized with single round amplified targets. Finally, the reliability of using a universal RNA reference for two-channel microarrays was tested and the results suggest that comparisons of multiple experimental conditions using the same control can be accurate.     

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.     

Comparison of gene expression measurements from cDNA and 60-mer oligonucleotide microarrays

As the data generated by microarray technology continue to amass, it is necessary to compare and combine gene expression data from different platforms. To evaluate the performance of cDNA and long oligonucleotide (60-mer) arrays, we generated gene expression profiles for two cancer cell lines and compared the data between the two platforms. All 6182 unique genes represented on both platforms were included in the analysis. A limited correlation (r = 0.4708) was obtained and the difference in measurement of low-expression genes was considered to contribute to the limited correlation. Further restriction of the data set to differentially expressed genes detected in cDNA microarrays (1205 genes) and oligonucleotide arrays (1325 genes) showed modest correlations of 0.7076 and 0.6441 between the two platforms. Quantitative real-time PCR measurements of a set of 10 genes showed better correlation with oligonucleotide arrays. Our results demonstrate that there is substantial variation in the data generated from cDNA and 60-mer oligonucleotide arrays. Although general agreement was observed in measurements of differentially expressed genes, we suggest that data from different platforms could not be directly amassed.     

Microarray results improve significantly as hybridization approaches equilibrium

Dual-channel long oligonucleotide microarrays are in widespread use. Although much attention has been given to proper experimental design and analysis regarding long oligonucleotide microarrays, relatively little information is available concerning the optimization of protocols. We carried out a series of microarray experiments designed to investigate the effects of different levels of target concentration and hybridization times using a long oligonucleotide library. Based on principles developed from nucleic acid renaturation kinetics studies, we show that increasing the time of hybridization from 18 h to 42 h and 66 h, especially when lower than optimal concentrations of target were used, significantly improved the quality of the microarray results. Longer hybridization times significantly increased the number of spots detected, signal-to-noise ratios, and the number of differentially expressed genes and correlations among replicate arrays. We conclude that at 18 h of incubation, target-to-probe hybridization has not reached equilibrium and that a relatively high proportion of nonspecific hybridization occurs. This result is striking, given that most, if not all, published microarray protocols stipulate 8-24 h for hybridization. Using shorter than optimal hybridization times (i.e., not allowing hybridization to reach equilibrium) has the consequence of underestimating the fold change of differentially expressed genes and of missing less represented sequences.     

Using expression arrays for copy number detection: an example from E. coli

Background  

The sequencing of many genomes and tiling arrays consisting of millions of DNA segments spanning entire genomes have made high-resolution copy number analysis possible. Microarray-based comparative genomic hybridization (array CGH) has enabled the high-resolution detection of DNA copy number aberrations. While many of the methods and algorithms developed for the analysis microarrays have focused on expression analysis, the same technology can be used to detect genetic alterations, using for example standard commercial Affymetrix arrays. Due to the nature of the resultant data, standard techniques for processing GeneChip expression experiments are inapplicable.     

New tools for oligonucleotide fingerprinting

Oligonucleotide fingerprinting is an attractive, high-throughput complement to tag sequencing methods to determine the spectrum and abundance of genes in cDNA libraries. This method currently relies on the sequential hybridizations of short, radioactively labeled DNA oligonucleotides to clone arrays. Here, we describe a new environment that substantially improves this technology. Fluorescently labeled peptide nucleic acid (PNA) oligonucleotides are used as hybridization probes. Hybridization results are recorded with a large-field, high-resolution laser scanner developed for this purpose. Automated image analysis allows easy handling of large numbers of hybridization images. Signal interference effects, which limit the gridding density in the radioactive approach, are strongly reduced. The sensitivity of the fluorescence detection demonstrated permits the convenient use of nylon membranes. Hybridization data quality is improved, and its generation is substantially accelerated, simplified, and less expensive.