Data-driven normalization strategies for high-throughput quantitative RT-PCR
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:10.1186/1471-2105-10-110Published: 19 April 2009
Additional file 1:
Figure S1. Expression Profiles for Four Genes Normalized by Different approaches.
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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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