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  Algorithm-driven Artifacts in median polish summarization of Microarray data

Giorgi, F. M., Bolger, A. M., Lohse, M., & Usadel, B. (2010). Algorithm-driven Artifacts in median polish summarization of Microarray data. BMC Bioinformatics, 11, 553. doi:10.1186/1471-2105-11-553.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-240F-2 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0014-2410-B
Genre: Journal Article

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Giorgi, F. M.1, Author              
Bolger, A. M.1, Author              
Lohse, M.1, Author              
Usadel, B.1, Author              
Affiliations:
1Integrative Carbon Biology, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society, ou_1753329              

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Free keywords: density oligonucleotide array genechip expression measures probe level data normalization coexpression networks patterns biology cancer
 Abstract: Background: High-throughput measurement of transcript intensities using Affymetrix type oligonucleotide microarrays has produced a massive quantity of data during the last decade. Different preprocessing techniques exist to convert the raw signal intensities measured by these chips into gene expression estimates. Although these techniques have been widely benchmarked in the context of differential gene expression analysis, there are only few examples where their performance has been assessed in respect to coexpression-based studies such as sample classification. Results: In the present paper we benchmark the three most used normalization procedures (MAS5, RMA and GCRMA) in the context of inter-array correlation analysis, confirming and extending the finding that RMA and GCRMA consistently overestimate sample similarity upon normalization. We determine that median polish summarization is responsible for generating a large proportion of these over-similarity artifacts. Furthermore, we show that most affected probesets show also internal signal disagreement, and tend to be composed by individual probes hitting different gene transcripts. We finally provide a correction to the RMA/GCRMA summarization procedure that massively reduces inter-array correlation artifacts, without affecting the detection of differentially expressed genes. Conclusions: We propose tRMA as a modification of RMA to normalize microarray experiments for correlation-based analysis.

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Language(s): eng - English
 Dates: 2010-11-112010
 Publication Status: Published in print
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 Identifiers: ISI: ISI:000285045600001
DOI: 10.1186/1471-2105-11-553
ISSN: 1471-2105 (Electronic) 1471-2105 (Linking)
URI: ://000285045600001 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2998528/pdf/1471-2105-11-553.pdf?tool=pmcentrez
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Title: BMC Bioinformatics
Source Genre: Journal
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Pages: - Volume / Issue: 11 Sequence Number: - Start / End Page: 553 Identifier: -