Data-Driven Normalization Strategies for High-Throughput Quantitative RT-PCR
Irvine, Katharine M
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CitationMar, Jessica C., Yasumasa Kimura, Kate Schroder, Katharine M. Irvine, Yoshihide Hayashizaki, Harukazu Suzuki, David Hume, and John Quackenbush. 2009. Data-driven normalization strategies for high-throughput quantitative RT-PCR. BMC Bioinformatics 10:110.
AbstractBackground: High-throughput real-time quantitative reverse transcriptase polymerase chain reaction (qPCR) is a widely used technique in experiments where expression patterns of genes are to be profiled. Current stage technology allows the acquisition of profiles for a moderate number of genes (50 to a few thousand), and this number continues to grow. The use of appropriate normalization algorithms for qPCR-based data is therefore a highly important aspect of the data preprocessing pipeline. Results: We present and evaluate two data-driven normalization methods that directly correct for technical variation and represent robust alternatives to standard housekeeping gene-based approaches. We evaluated the performance of these methods against a single gene housekeeping gene method and our results suggest that quantile normalization performs best. These methods are implemented in freely-available software as an R package qpcrNorm distributed through the Bioconductor project. Conclusion: The utility of the approaches that we describe can be demonstrated most clearly in situations where standard housekeeping genes are regulated by some experimental condition. For large qPCR-based data sets, our approaches represent robust, data-driven strategies for normalization.
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