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      Key Issues in Conducting a Meta-Analysis of Gene Expression Microarray Datasets

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      Author(s): * , , ,
      Publication date (Electronic):
      Journal: PLoS Medicine
      Publisher: Public Library of Science

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          Abstract

          Adaikalavan Ramasamy and colleagues outline seven key issues and suggest a stepwise approach in conducting a meta-analysis of microarray datasets.

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          A gene-coexpression network for global discovery of conserved genetic modules.

          Joshua M. Stuart, Eran Segal, Daphne Koller … (2003)
          To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Many of these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentally confirmed the predictions implied by some of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.
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            Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.

            Y. H. Yang (2002)
            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.
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              Quantitative monitoring of gene expression patterns with a complementary DNA microarray.

              M Schena, D Shalon, R W Davis … (1995)
              A high-capacity system was developed to monitor the expression of many genes in parallel. Microarrays prepared by high-speed robotic printing of complementary DNAs on glass were used for quantitative expression measurements of the corresponding genes. Because of the small format and high density of the arrays, hybridization volumes of 2 microliters could be used that enabled detection of rare transcripts in probe mixtures derived from 2 micrograms of total cellular messenger RNA. Differential expression measurements of 45 Arabidopsis genes were made by means of simultaneous, two-color fluorescence hybridization.
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                Author and article information

                Journal
                Journal ID (nlm-ta): PLoS Med
                Journal ID (publisher-id): pmed
                Journal ID (publisher-id): plme
                Journal ID (pmc): plosmed
                Title: PLoS Medicine
                Publisher: Public Library of Science (San Francisco, USA )
                ISSN (Print): 1549-1277
                ISSN (Electronic): 1549-1676
                Publication date (Print): September 2008
                Publication date (Electronic): 2 September 2008
                Volume: 5
                Issue: 9
                Electronic Location Identifier: e184
                Author notes
                * To whom correspondence should be addressed. E-mail: ramasamy@ 123456stats.ox.ac.uk
                Article
                Publisher ID: 07-PLME-GG-1196R3
                DOI: 10.1371/journal.pmed.0050184
                PMC ID: 2528050
                PubMed ID: 18767902
                SO-VID: 5681ead2-9fbb-4c94-9b11-88cab7e0c598
                Copyright © Copyright: © 2008 Ramasamy et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                History
                Page count
                Pages: 13
                Categories
                Subject: Guidelines and Guidance
                Subject: Cell Biology
                Subject: Computational Biology
                Subject: Evidence-Based Healthcare
                Subject: Genetics and Genomics
                Subject: Mathematics
                Subject: Molecular Biology
                Subject: Public Health and Epidemiology
                Subject: Genetics
                Subject: Research Methods
                Subject: Research Design
                Subject: Statistics
                Custom metadata
                citation Ramasamy A, Mondry A, Holmes CC, Altman DG (2008) Key issues in conducting a meta-analysis of gene expression microarray datasets. PLoS Med 5(9): e184. doi: 10.1371/journal.pmed.0050184

                ScienceOpen disciplines: Medicine
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                ScienceOpen disciplines: Medicine

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