| Summary: | 碩士 === 國立臺北大學 === 統計學系 === 100 === The real-time PCR (real-time polymerase chain reaction) is a common technique for evaluating the gene expression. This technique can provide very sensitive and accurate results since it is monitored instantaneously and also performs a quantitative analysis for the target gene. It has become a widespread technique in analyzing gene expressions. There are two methods to quantify the real-time PCR gene expression, relative and absolute quantification. Owing to cost and available sources, the relative quantification is the more commonly used method. However, the relative quantification requires a housekeeping gene as an internal control gene to normalize the target gene expression. Andersen et al. (2004) and Dheda et al. (2004) pointed out the gene expression of housekeeping gene may be unstable not only due to the biological variation, but also different experimental conditions. Hence, we discuss the feasibility of implementing the normalization method for high density oligonucleotide array to the relative quantification in real-time PCR.
Three common normalization methods for high density oligonucleotide array, the scaling normalization (Affymetrix, 2002), the invariant set normalization (Li and Wong, 2001) and the quantile normalization (Bolstad et al. 2003), are discussed. Owing to large differences in data characteristics, Monte Carlo simulations are used to evaluate the performance of these normalizations to the real-time PCR. Four indices are used to assess the performance. Furthermore, a real data is used to illustrate the feasibility of these normalizations to the real-time PCR. We find that instead of using the housekeeping gene, the scaling normalization is a good choice for relative quantification in real-time PCR.
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