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SASqPCR: Robust and Rapid Analysis of RT-qPCR Data in SAS

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  • Daijun Ling

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.

Suggested Citation

  • Daijun Ling, 2012. "SASqPCR: Robust and Rapid Analysis of RT-qPCR Data in SAS," PLOS ONE, Public Library of Science, vol. 7(1), pages 1-5, January.
    • Handle: RePEc:plo:pone00:0029788
    DOI: 10.1371/journal.pone.0029788
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    Cited by:

    1. Carlos S Nascimento & Leandro T Barbosa & Claudson Brito & Roberta P M Fernandes & Renata S Mann & Ana Paula G Pinto & Haniel C Oliveira & Mike V Dodson & Simone E F Guimarães & Marcio S Duarte, 2015. "Identification of Suitable Reference Genes for Real Time Quantitative Polymerase Chain Reaction Assays on Pectoralis major Muscle in Chicken (Gallus gallus )," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-15, May.
    2. Mikhail V Matz & Rachel M Wright & James G Scott, 2013. "No Control Genes Required: Bayesian Analysis of qRT-PCR Data," PLOS ONE, Public Library of Science, vol. 8(8), pages 1-12, August.

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