Open AccessThe choice of reference gene affects statistical efficiency in quantitative PCR data analysis
Author(s) -
Yi Guo,
Michael L. Pennell,
Dennis K. Pearl,
Thomas J. Knobloch,
Soledad Fernández,
Christopher M. Weghorst
Publication year - 2013
Publication title -
biotechniques
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.617
H-Index - 131
eISSN - 1940-9818
pISSN - 0736-6205
DOI - 10.2144/000114090
Subject(s) - normalization (sociology) , database normalization , statistical power , data set , statistical analysis , efficiency , computational biology , analysis of variance , biology , computer science , data mining , statistics , genetics , mathematics , artificial intelligence , pattern recognition (psychology) , estimator , sociology , anthropology
Quantitative polymerase chain reaction (qPCR), a highly sensitive method of measuring gene expression, is widely used in biomedical research. To produce reliable results, it is essential to use stably expressed reference genes (RGs) for data normalization so that sample-to-sample variation can be controlled. In this study, we examine the effect of different RGs on statistical efficiency by analyzing a qPCR data set that contains 12 target genes and 3 RGs. Our results show that choosing the most stably expressed RG for data normalization does not guarantee reduced variance or improved statistical efficiency. We also provide a formula for determining when data normalization will improve statistical efficiency and hence increase the power of statistical tests in data analysis.
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