Methodology article
Data-driven normalization strategies for high-throughput quantitative RT-PCR
-
* Corresponding author: John Quackenbush johnq@jimmy.harvard.edu
1 Department of Biostatistics, Harvard School of Public Health, 677 Huntington Avenue, Boston, Massachusetts 02115, USA
2 RIKEN, Omics Science Center, Yokohama Institute, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, 230-0045, Japan
3 Institute for Molecular Biosciences, University of Queensland, St Lucia, Brisbane QLD 4072, Australia
4 Roslin Institute, University of Edinburgh, Roslin Midlothian EH25 9PS, Scotland, UK
5 Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 44 Binney Street, Boston, Massachusetts 02115, USA
6 Department of Cancer Biology, Dana-Farber Cancer Institute, 44 Binney Street, Boston, Massachusetts 02115, USA
BMC Bioinformatics 2009, 10:110 doi:10.1186/1471-2105-10-110
Published: 19 April 2009Abstract
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.