chipPCR-package {chipPCR}

Toolkit of functions to pre-process amplification data

Description

A toolkit of functions to pre-process amplification curve data. Amplification data can be obtained from conventional PCR reactions or isothermal amplification reactions. Contains functions to normalize and baseline amplification curves, a routine to detect the start of an amplification reaction, several smoothers for amplification data, a function to distinguish positive and negative amplification reactions and a function to determine the amplification efficiency. The smoothers are based on LOWESS, moving average, cubic splines, Savitzky-Golay and others. In addition the first approximate approximate derivative maximum (FDM) and second approximate derivative maximum (SDM) can be calculated by a 5-point-stencil as quantification points from real-time amplification curves. chipPCR contains data sets of experimental nucleic acid amplification systems including the 'VideoScan' 'HCU' and a capillary convective PCR (ccPCR) system. The amplification data were generated by helicase dependent amplification (HDA) or polymerase chain reaction (PCR) under various temperature conditions. As detection system intercalating dyes (EvaGreen, SYBR Green) and hydrolysis probes (TaqMan) were used. The latest source code is available via: https://github.com/PCRuniversum/chipPCR

Details

Package:	chipPCR
Type:	Package
Version:	0.0.8-12
Date:	2017-06-22
License:	GPL-3

bg.max can be used to remove missing values in amplification curve data. amptester tests if an amplification is positive. fixNA is used to impute missing values from a data column. CPP can be used to normalize curve data, to remove background, to remove outliers and further steps. Contains further functions to smooth the data by different functions including LOWESS, Moving Average, Friedman's SuperSmoother, Cubic Spline and Savitzky-Golay smoothing.

For more exhaustive description see the vignette (vignette("chipPCR")).

Author(s)

Stefan Roediger, Michal Burdukiewicz

Maintainer: Stefan Roediger <[e-mail]>

References

A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. S. Roediger, P. Schierack, A. Boehm, J. Nitschke, I. Berger, U. Froemmel, C. Schmidt, M. Ruhland, I. Schimke, D. Roggenbuck, W. Lehmann and C. Schroeder. Advances in Biochemical Bioengineering/Biotechnology. 133:33–74, 2013.

Nucleic acid detection based on the use of microbeads: a review. S. Roediger, C. Liebsch, C. Schmidt, W. Lehmann, U. Resch-Genger, U. Schedler, P. Schierack. Microchim Acta 2014:1–18. DOI: 10.1007/s00604-014-1243-4

Roediger S, Boehm A, Schimke I. Surface Melting Curve Analysis with R. The R Journal 2013;5:37–53.

Spiess, A.-N., Deutschmann, C., Burdukiewicz, M., Himmelreich, R., Klat, K., Schierack, P., Roediger, S., 2014. Impact of Smoothing on Parameter Estimation in Quantitative DNA Amplification Experiments. Clinical Chemistry clinchem.2014.230656. doi:10.1373/clinchem.2014.230656

See Also

qpcR.news.

Examples

# Example: A simple function to test for a background range.
# Data were taken form the chipPCR C17 data set.
data(C17)
plot(C17[, 2], C17[,  3], xlab = "time [min]", ylab = "Fluorescence", 
      pch = 20)
res <- bg.max(C17[, 2], C17[, 3], bg.corr = 1.4, bg.start = 3)
abline(v = c(slot(res, "bg.start"), slot(res, "bg.stop")), col = c(1,2))
abline(h = slot(res, "fluo"), col = "blue")