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Comparative Microarray Analysis

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Forgot password? Old Password. New Password. The presentation is appropriate for readers from biology or bioinformatics. After presenting a framework for the design of microarray-driven functional genomics experiments, the book discusses the foundations for analyzing microarray data sets, genomic data-mining, the creation of standardized nomenclature and data models, clinical applications of functional genomics research, and the future of functional genomics.

This book should be required reading for any future and present post-genomic researcher Hagit Shatkay and Mark Craven. Bruce R.

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Search Search. Search Advanced Search close Close. Preview Preview. Butte A Bradford Book. Request Permissions Exam copy. They report inconsistencies in sequence fidelity of the spotted microarrays, variability of differential expression, low specificity of cDNA probes, discrepancy in fold-change calculations, and lack of probe specificity for different isoforms of a gene. Ntzani and Ioannidis 24 examined 84 large-scale microarray expression data sets that address major clinical outcomes including death, metastasis, recurrence, and response to therapy.

They found that these studies show variable prognostic performance. Tan et al 25 examined gene expression measurements generated from identical RNA preparations that were obtained using 3 commerically available microarray platforms from Affymetrix, Amersham, and Agilent. Correlations in gene expression levels and comparisons for significant gene expression changes in this subset showed considerable divergence across the different platforms. Michiels et al 26 reanalyzed data from the 7 largest published studies that have attempted to predict prognosis of patients with cancer on the basis of DNA microarray analysis.

The results reveal that the list of genes identified as predictors of prognosis was highly unstable and molecular signatures were strongly dependent on the selection of patients in the training sets. In addition, 5 of the 7 studies did not classify patients better than chance. The poor specificity and reproducibility are not surprising considering all the experimental variables that affect the quality of the data sets. These include variations in the laboratories, individuals, probe labeling, biochemical reactions, scanners, and lasers.

Microarrays for an Integrative Genomics

The specificity of the discovery should be stringent when the data sets consist of tens of thousands of genes and contain a predominant majority of noise. This example illustrates the limitations of statistical significance when noise is predominant. Microarrays assay for the relative expression levels of a cDNA 1 in a biological sample as compared with another and 2 relative to other cDNAs within the same sample.

The accuracy of fold changes is critical for data analysis. The results of Kothapalli et al 23 reveal poor reproducibility and discrepancies of fold-change calculations between microarrays interarray. Furthermore, the accuracy of calculations of fold changes of genes within a single microarray intra-array is not known.

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Low specificity, the preponderance and heterogeneity of noise, and inaccurate fold-change calculations impose significant limitations on data analysis. For example, apparent molecular classifications may be caused by data set—specific noise and the results of 1 laboratory may disintegrate when tested independently. Recent reports describe mathematical models that shed light on the behavior of noise in microarray data sets and algorithms that discover highly specific states of genetic expression up-regulated or down-regulated from genomewide expression profiling.

The preponderance of noise implies that 1 the overwhelming majority of the genes on the array are not differentially expressed between samples true negatives and 2 the truly negative genes generate false-positive expression data noise. Noise heterogeneity implies that the distribution of noise varies between data sets depending on quality. These principles may be summarized as follows:. Each sample vs reference comparison generates tens of thousands of expression ratios.

Even when the expression levels of the genes do not differ between the sample and reference, the predominant majority of their measured expression ratios are not equal to 1 noise, artifacts, or false positives. The distributions of the false positives vary widely between experiments; the variability is determined by quality. The mathematical tools generate highly specific discovery by modeling and filtering noise Figure 2.

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The use of mathematical modeling and filters is common; to name a few examples, engineers apply filters to solve problems of noise in cellular telephones, digital music, and digital television. Highly specific genome-scale discovery of states of genetic expression has applications in all aspects of biology and medicine; it facilitates hypothesis-driven research and sets the stage for studies in systems biology. First is the model of a single genetic lesion causing a phenotype; an example is sickle cell disease.

A second model is that of several genotypes causing the same phenotype; examples include malignant brain tumors and Alzheimer disease.

A third model is that of a single genetic lesion causing distinct phenotypes depending on polymorphisms; examples include hereditary Creutzfeldt-Jakob disease and fatal familial insomnia. The idea that molecular systems, and not single genes, create phenotypes has important biological and therapeutic implications.

Microarrays for an integrative genomics

The majority of clinical trials that have targeted single or a few genes have failed; most compounds that show efficacy in preclinical experiments and phase 1 and phase 2 clinical trials turn out to be ineffective in very expensive phase 3 trials. Hopefully, systems biology will improve the decision making for the transition to phase 3 clinical trials.


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Meningiomas disturb this balanced expression to promote their phenotypes. Targeting single genes or single pathways is likely to fail because molecular systems have redundant molecules or pathways that bypass the blockade. It is intuitive that targets selected based on molecular systems are more likely to be clinically effective than targets selected based on single molecules or pathways. Microarrays can be extremely useful for many biological fields, particularly clinical neurology and systems biology, but they can also be very misleading.

Not unlike many fields in physics, the full potential of microarrays awaits advances in mathematics. We ought to step back to the drawing board to develop better tools for data analysis. Correspondence: Hassan M. All Rights Reserved.


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  • Download PDF Top of Article Abstract The experimental system Potential applications of microarrays in biology and medicine Pitfalls of microarrays The nature of the problem Highly specific expression discovery Significance and future directions Article Information References. Figure 1. View Large Download.