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BMC Bioinformatics. 2009 Apr 19;10:110. doi: 10.1186/1471-2105-10-110.

Data-driven normalization strategies for high-throughput quantitative RT-PCR.

Author information

1
Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, MA 02115, USA. jess@jimmy.harvard.edu

Abstract

BACKGROUND:

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.

PMID:
19374774
PMCID:
PMC2680405
DOI:
10.1186/1471-2105-10-110
[Indexed for MEDLINE]
Free PMC Article

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