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Journal Article

Algorithm-driven Artifacts in median polish summarization of Microarray data

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons97169

Giorgi,  F. M.
Integrative Carbon Biology, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97079

Bolger,  A. M.
Integrative Carbon Biology, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97281

Lohse,  M.
Integrative Carbon Biology, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97455

Usadel,  B.
Integrative Carbon Biology, Department Stitt, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

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Citation

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.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0014-240F-2
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.