Statistical modeling for selecting housekeeper genes

被引：0
作者：
Aniko Szabo
Charles M Perou
Mehmet Karaca
Laurent Perreard
John F Quackenbush
Philip S Bernard
机构：
[1] Department of Oncological Sciences,Lineberger Comprehensive Cancer Center and Department of Genetics
[2] Huntsman Cancer Institute,Department of Pathology
[3] University of North Carolina,undefined
[4] ARUP Laboratories Inc,undefined
[5] University of Utah,undefined
来源：
Genome Biology | / 5卷
关键词：
Mean Square Error; Tissue Type; Additional Data File; Primary Breast Tumor; Breast Tumor Sample;
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摘要：
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.
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