Description Details Author(s) References See Also Examples
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
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")
).
Stefan Roediger, Michal Burdukiewicz
Maintainer: Stefan Roediger <stefan.roediger@b-tu.de>
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
qpcR.news.
1 2 3 4 5 6 7 8 | # 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")
|
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