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

Jessica C. Mar, Yasumasa Kimura, Kate Schroder, Katharine M. Irvine, Yoshihide Hayashizaki, Harukazu Suzuki, David Hume, John Quackenbush

Research output: Contribution to journalArticle

65 Citations (Scopus)

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.

Original languageEnglish (US)
Article number110
JournalBMC Bioinformatics
Volume10
DOIs
StatePublished - Apr 19 2009
Externally publishedYes

Fingerprint

Data-driven
High Throughput
Normalization
Essential Genes
Genes
Reverse Transcriptase Polymerase Chain Reaction
Throughput
Gene
Polymerase Chain Reaction
Polymerase chain reaction
RNA-Directed DNA Polymerase
Reverse
Data Preprocessing
Software
Quantile
Technology
Gene Expression
Strategy
Continue
Pipelines

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Structural Biology
  • Applied Mathematics

Cite this

Mar, J. C., Kimura, Y., Schroder, K., Irvine, K. M., Hayashizaki, Y., Suzuki, H., ... Quackenbush, J. (2009). Data-driven normalization strategies for high-throughput quantitative RT-PCR. BMC Bioinformatics, 10, [110]. https://doi.org/10.1186/1471-2105-10-110

Data-driven normalization strategies for high-throughput quantitative RT-PCR. / Mar, Jessica C.; Kimura, Yasumasa; Schroder, Kate; Irvine, Katharine M.; Hayashizaki, Yoshihide; Suzuki, Harukazu; Hume, David; Quackenbush, John.

In: BMC Bioinformatics, Vol. 10, 110, 19.04.2009.

Research output: Contribution to journalArticle

Mar, JC, Kimura, Y, Schroder, K, Irvine, KM, Hayashizaki, Y, Suzuki, H, Hume, D & Quackenbush, J 2009, 'Data-driven normalization strategies for high-throughput quantitative RT-PCR', BMC Bioinformatics, vol. 10, 110. https://doi.org/10.1186/1471-2105-10-110
Mar JC, Kimura Y, Schroder K, Irvine KM, Hayashizaki Y, Suzuki H et al. Data-driven normalization strategies for high-throughput quantitative RT-PCR. BMC Bioinformatics. 2009 Apr 19;10. 110. https://doi.org/10.1186/1471-2105-10-110
Mar, Jessica C. ; Kimura, Yasumasa ; Schroder, Kate ; Irvine, Katharine M. ; Hayashizaki, Yoshihide ; Suzuki, Harukazu ; Hume, David ; Quackenbush, John. / Data-driven normalization strategies for high-throughput quantitative RT-PCR. In: BMC Bioinformatics. 2009 ; Vol. 10.
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