A standard curve based method for relative real time PCR data processing - PubMed (original) (raw)

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

Alexey Larionov et al. BMC Bioinformatics. 2005.

Abstract

Background: 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.

Results: 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.

Conclusion: 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.

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Figures

Figure 1

Figure 1

Noise filtering. Axes: vertical – fluorescence, horizontal – cycle number, A Source data, B Smoothing, C Baseline subtraction, D Amplitude normalization

Figure 2

Figure 2

Direct calculation of crossing points.

Figure 3

Figure 3

Expression of Cyclin B1 mRNA in breast cancer biopsies. The observed decrease of Cyclin B1 expression after treatment was expected in most but not all cases. Bars show actual 95% confidence intervals estimated by the described statistical procedure in a set of real clinical specimens (NB – these are confidence intervals for intra-assay PCR variation only).

Figure 4

Figure 4

Distribution of crossing points in PCR replicas. Axes: vertical – relative frequency (%), horizontal – crossing points. Histogram represents a typical crossing points' distribution in 96× replica (Plate 1 from Table 2). The Kolmogorov-Smirnov test has not revealed significant deviations from the Normal distribution. The red line shows a Normal fit.

Figure 5

Figure 5

Transformation of normal distribution through data processing. Axes: vertical – relative frequency (%), horizontal – results. Red lines show Normal fits. A: At CPs' CV 0.5% the deviations from normality were not detectable using the Kolmogorov-Smirnov test. B: At CPs' CV 1% the deviations from normality were not detectable in non-normalized values though moderate deviations were detectable in final results. C: At CPs' CV 2% deviations from normality were detectable in both non-normalized values and in final results.

Figure 6

Figure 6

Effect of staining with SYBR Green 1 on PCR gel. A: Before staining. B: After staining. Before electrophoresis SYBR Green1 was added to marker but not to samples.

Figure 7

Figure 7

Effect of different factors on plateau position. A: More enzyme in blue than in red samples B: More primers in blue than in red samples C: Domed and plain caps

Figure 8

Figure 8

Optical factors affect the plateau scattering. SYBR Green real time PCR in frosted plates (green) and white plates (blue). Frosted plates cause increased plateau scattering because of inconsistent reflection and refraction (Reproduced from [18], with ABgene® permission).

Figure 9

Figure 9

Effect of amplitude normalization on standard curve.

Figure 10

Figure 10

Effect of amplitude normalization on plateau scattering in 96× replica. Axes: vertical – Fluorescence, horizontal – Cycle. Data for plate 3 from Table 2.

Figure 11

Figure 11

PCR set up.

Figure 12

Figure 12

Computer simulation of PCR data processing. Computer simulation of PCR data processing at 1% CV in crossing points (see Methods for details).

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References

    1. Bustin SA. Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. J Mol Endocrinol. 2002;29:23–39. doi: 10.1677/jme.0.0290023. - DOI - PubMed
    1. Muller PY, Janovjak H, Miserez AR, Dobbie Z. Processing of gene expression data generated by quantitative real-time RT-PCR. Biotechniques. 2002;32:1372–4, 1376, 1378-9. - PubMed
    1. Pfaffl MW. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 2001;29:e45. doi: 10.1093/nar/29.9.e45. - DOI - PMC - PubMed
    1. Pfaffl MW, Horgan GW, Dempfle L. Relative expression software tool (REST) for group-wise comparison and statistical analysis of relative expression results in real-time PCR. Nucleic Acids Res. 2002;30:e36. doi: 10.1093/nar/30.9.e36. - DOI - PMC - PubMed
    1. Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25:402–408. doi: 10.1006/meth.2001.1262. - DOI - PubMed

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