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Statistical modeling for selecting housekeeper genes

Aniko Szabo1*, Charles M Perou2, Mehmet Karaca2, Laurent Perreard3, John F Quackenbush4 and Philip S Bernard34

Author Affiliations

1 Department of Oncological Sciences, Huntsman Cancer Institute, Salt Lake City, UT 84112, USA

2 Lineberger Comprehensive Cancer Center and Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA

3 ARUP Laboratories Inc., Salt Lake City, UT 84108, USA

4 Department of Pathology, University of Utah, Salt Lake City, UT 84112, USA

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Genome Biology 2004, 5:R59  doi:10.1186/gb-2004-5-8-r59

Published: 29 July 2004


There is a need for statistical methods to identify genes that have minimal variation in expression across a variety of experimental conditions. These 'housekeeper' genes are widely employed as controls for quantification of test genes using gel analysis and real-time RT-PCR. Using real-time quantitative RT-PCR, we analyzed 80 primary breast tumors for variation in expression of six putative housekeeper genes (MRPL19 (mitochondrial ribosomal protein L19), PSMC4 (proteasome (prosome, macropain) 26S subunit, ATPase, 4), SF3A1 (splicing factor 3a, subunit 1, 120 kDa), PUM1 (pumilio homolog 1 (Drosophila)), ACTB (actin, beta) and GAPD (glyceraldehyde-3-phosphate dehydrogenase)). We present appropriate models for selecting the best housekeepers to normalize quantitative data within a given tissue type (for example, breast cancer) and across different types of tissue samples.