Abstract
Reverse transcription quantitative real-time PCR (RT-qPCR) is a key method for measurement of relative gene expression. Analysis of RT-qPCR data requires many iterative computations for data normalization and analytical optimization. Currently no computer program for RT-qPCR data analysis is suitable for analytical optimization and user-controllable customization based on data quality, experimental design as well as specific research aims. Here I introduce an all-in-one computer program, SASqPCR, for robust and rapid analysis of RT-qPCR data in SAS. This program has multiple macros for assessment of PCR efficiencies, validation of reference genes, optimization of data normalizers, normalization of confounding variations across samples, and statistical comparison of target gene expression in parallel samples. Users can simply change the macro variables to test various analytical strategies, optimize results and customize the analytical processes. In addition, it is highly automatic and functionally extendable. Thus users are the actual decision-makers controlling RT-qPCR data analyses. SASqPCR and its tutorial are freely available at http://code.google.com/p/sasqpcr/downloads/list.
Highlights
Quantitative reverse transcription real-time polymerase chain reaction (RT-qPCR) is widely used in biomedical research and diagnostic applications for measurement of relative gene expression
SASqPCR contains 5 macros designed for different computational tasks including estimation of PCR efficiencies (%Efficiency), evaluation of expression stability of candidate reference genes (%Stability), determination of the optimal number of reference genes for robust data normalization (%Optimization), calculation of normalized expression ratios of target genes (%Normalization), and statistical comparisons of target gene expression between parallel samples (%Exp_R)
Estimation of PCR efficiency Correction of PCR efficiencies is highly recommended in quantification of gene expression using real-time PCR [4]
Summary
Quantitative reverse transcription real-time polymerase chain reaction (RT-qPCR) is widely used in biomedical research and diagnostic applications for measurement of relative gene expression. Standard statistical algorithms have to be iteratively implemented to determine what particular genes and how many genes should be selected from the reference candidates to achieve a better normalizer for a particular dataset [7,8]. No efficient and flexible program is available for RTqPCR data analysis with incorporation of the standard statistical algorithms for data-specific reference validation and analytical optimization. The program provides a dynamic interface for user-controllable customization based on data quality, experimental design as well as specific research aims. Users can perform unlimited iterative computations for testing various combinations of different analytical strategies or customized analytical processes. This manuscript briefly describes the working rationale of this program. The key algorithms, equations and annotated codes for the program have been described in a SASqPCR Tutorial document available at http://code.google.com/p/sasqpcr/ downloads/list; understanding of this knowledge as well as extensive SAS programming knowledge is not required for general users in application of SASqPCR
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