Open Access Highly Accessed Methodology article

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

Jessica C Mar1, Yasumasa Kimura2, Kate Schroder3, Katharine M Irvine3, Yoshihide Hayashizaki2, Harukazu Suzuki2, David Hume4 and John Quackenbush156*

Author Affiliations

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

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BMC Bioinformatics 2009, 10:110  doi:10.1186/1471-2105-10-110

Published: 19 April 2009

Additional files

Additional file 1:

Figure S1. Expression Profiles for Four Genes Normalized by Different approaches.

Format: EPS Size: 813KB Download file

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Additional file 2:

Table S1. Variances of 4 Gene Expression Profiles Normalized by Different Approaches.

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Additional file 3:

Tutorial and qpcrNorm Software. The R package qpcrNorm and a tutorial outlining its use.

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