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Microarray results: how accurate are they?

ABSTRACT: BACKGROUND:DNA microarray technology is a powerful technique that was recently developed in order to analyze thousands of genes in a short time. Presently, microarrays, or chips, of the cDNA type and oligonucleotide type are available from several sources. The number of publications in this area is increasing exponentially. RESULTS:In this study, microarray data obtained from two different commercially available systems were critically evaluated. Our analysis revealed several inconsistencies in the data obtained from the two different microarrays. Problems encountered included inconsistent sequence fidelity of the spotted microarrays, variability of differential expression, low specificity of cDNA microarray probes, discrepancy in fold-change calculation and lack of probe specificity for different isoforms of a gene. CONCLUSIONS:In view of these pitfalls, data from microarray analysis need to be interpreted cautiously.

SUBMITTER: Kothapalli R

PROVIDER: S-EPMC126254 | biostudies-literature | 2002 Aug

REPOSITORIES: biostudies-literature

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Microarray results: how accurate are they?

Kothapalli Ravi R Yoder Sean J SJ Mane Shrikant S Loughran Thomas P TP

BMC bioinformatics 20020823

<h4>Background</h4>DNA microarray technology is a powerful technique that was recently developed in order to analyze thousands of genes in a short time. Presently, microarrays, or chips, of the cDNA type and oligonucleotide type are available from several sources. The number of publications in this area is increasing exponentially.<h4>Results</h4>In this study, microarray data obtained from two different commercially available systems were critically evaluated. Our analysis revealed several inco ...[more]

PMID: 12194703

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Project description:BACKGROUND: Molecular profiling generates abundance measurements for thousands of gene transcripts in biological samples such as normal and tumor tissues (data points). Given such two-class high-dimensional data, many methods have been proposed for classifying data points into one of the two classes. However, finding very small sets of features able to correctly classify the data is problematic as the fundamental mathematical proposition is hard. Existing methods can find "small" feature sets, but give no hint how close this is to the true minimum size. Without fundamental mathematical advances, finding true minimum-size sets will remain elusive, and more importantly for the microarray community there will be no methods for finding them. RESULTS: We use the brute force approach of exhaustive search through all genes, gene pairs (and for some data sets gene triples). Each unique gene combination is analyzed with a few-parameter linear-hyperplane classification method looking for those combinations that form training error-free classifiers. All 10 published data sets studied are found to contain predictive small feature sets. Four contain thousands of gene pairs and 6 have single genes that perfectly discriminate. CONCLUSION: This technique discovered small sets of genes (3 or less) in published data that form accurate classifiers, yet were not reported in the prior publications. This could be a common characteristic of microarray data, thus making looking for them worth the computational cost. Such small gene sets could indicate biomarkers and portend simple medical diagnostic tests. We recommend checking for small gene sets routinely. We find 4 gene pairs and many gene triples in the large hepatocellular carcinoma (HCC, Liver cancer) data set of Chen et al. The key component of these is the "placental gene of unknown function", PLAC8. Our HMM modeling indicates PLAC8 might have a domain like part of lP59's crystal structure (a Non-Covalent Endonuclease lii-Dna Complex). The previously identified HCC biomarker gene, glypican 3 (GPC3), is part of an accurate gene triple involving MT1E and ARHE. We also find small gene sets that distinguish leukemia subtypes in the large pediatric acute lymphoblastic leukemia cancer set of Yeoh et al.

Project description:BACKGROUND:Avian influenza virus (AIV), infectious bronchitis virus (IBV), and Newcastle disease virus (NDV) are important avian pathogens that can cause enormous economic loss on the poultry industry. Different respiratory etiological agents may induce similar clinical signs that make differential diagnosis difficult. Importantly, AIV brings about severe threat to human public health. Therefore, a novel method that can distinguish these viruses quickly and simultaneously is urgently needed. RESULTS:In this study, an oligonucleotide microarray system was developed. AIV, including H5, H7, and H9 subtypes; NDV; and IBV were simultaneously detected and differentiated on a microarray. Three probes specific for AIV, NDV, and IBV, as well as three other probes for differentiating H5, H7, and H9 of AIV, were first designed and jet-printed to predetermined locations of initiator-integrated poly(dimethylsiloxane) for the synchronous detection of the six pathogens. The marked multiplex reverse transcription polymerase chain reaction (PCR) products were hybridized with the specific probes, and the results of hybridization were read directly with the naked eyes. No cross-reaction was observed with 10 other subtypes of AIV and infectious bursal disease virus, indicating that the oligonucleotide microarray assay was highly specific. The sensitivity of the method was at least 100 times higher than that of the conventional PCR, and the detection limit of NDV, AIV, H5, H7, and H9 can reach 0.1 EID50 (50% egg infective dose), except that of IBV, which was 1 EID50 per reaction. In the validation of 93 field samples, AIV, IBV, and NDV were detected in 53 (56.99%) samples by oligonucleotide microarray and virus isolation and in 50 (53.76%) samples by conventional PCR. CONCLUSIONS:We have successfully developed an approach to differentiate AIV, NDV, IBV, H5, H7, and H9 subtypes of AIV using oligonucleotide microarray. The microarray is an accurate, high-throughput, and relatively simple method for the rapid detection of avian respiratory viral diseases. It can be used for the epidemiological surveillance and diagnosis of AIV, IBV, and NDV.

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Microarray results: how accurate are they?

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Microarray results: how accurate are they?

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