A standard curve based method for relative real time PCR data processing

• Published in: BMC bioinformatics 2005-03, Vol.6 (1), p.62-62, Article 62
• Main Authors: Larionov, Alexey, Krause, Andreas, Miller, William
• Format: Article
• Language: English
• Subjects: Algorithms; Breast Neoplasms - metabolism; Cloning, Molecular; Computational Biology - methods; Computer Graphics; Data Interpretation, Statistical; DNA Primers - chemistry; DNA, Complementary - metabolism; Fluorescent Dyes - pharmacology; Gene Expression Profiling; Gene Expression Regulation; Gene Expression Regulation, Neoplastic; Humans; Indicators and Reagents; Methodology; Polymerase Chain Reaction; Regression Analysis; Reproducibility of Results; Reverse Transcriptase Polymerase Chain Reaction - methods; RNA, Complementary; Sensitivity and Specificity; Software
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-6-62

Currently real time PCR is the most precise method by which to measure gene expression. The method generates a large amount of raw numerical data and processing may notably influence final results. The data processing is based either on standard curves or on PCR efficiency assessment. At the moment, the PCR efficiency approach is preferred in relative PCR whilst the standard curve is often used for absolute PCR. However, there are no barriers to employ standard curves for relative PCR. This article provides an implementation of the standard curve method and discusses its advantages and limitations in relative real time PCR. We designed a procedure for data processing in relative real time PCR. The procedure completely avoids PCR efficiency assessment, minimizes operator involvement and provides a statistical assessment of intra-assay variation. The procedure includes the following steps. (I) Noise is filtered from raw fluorescence readings by smoothing, baseline subtraction and amplitude normalization. (II) The optimal threshold is selected automatically from regression parameters of the standard curve. (III) Crossing points (CPs) are derived directly from coordinates of points where the threshold line crosses fluorescence plots obtained after the noise filtering. (IV) The means and their variances are calculated for CPs in PCR replicas. (V) The final results are derived from the CPs' means. The CPs' variances are traced to results by the law of error propagation. A detailed description and analysis of this data processing is provided. The limitations associated with the use of parametric statistical methods and amplitude normalization are specifically analyzed and found fit to the routine laboratory practice. Different options are discussed for aggregation of data obtained from multiple reference genes. A standard curve based procedure for PCR data processing has been compiled and validated. It illustrates that standard curve design remains a reliable and simple alternative to the PCR-efficiency based calculations in relative real time PCR.