Quantitative Polymerase Chain Reaction (qPCR) Data Mining and
Machine Learning Toolkit
c(person("Stefan", "Roediger",
email = "stefan.roediger@b-tu.de",
comment = c(ORCID = "0000-0002-1441-6512"),
role = c("cre", "aut")),
person("Michal", "Burdukiewicz",
email = "michalburdukiewicz@gmail.com",
comment = c(ORCID = "0000-0001-8926-582X"),
role = c("aut")),
person("Andrej-Nikolai", "Spiess",
email = "a.spiess@uke.de",
role = c("aut")),
person("Konstantin A.", "Blagodatskikh",
email = "k.blag@yandex.ru",
role = c("aut")),
person("Dominik", "Rafacz",
email = "dominikrafacz@gmail.com",
role = c("ctb")))
Extracts features from amplification curve data of quantitative Polymerase Chain Reactions (qPCR) (Pabinger S. et al. (2014) <doi:10.1016/j.bdq.2014.08.002>) for machine learning purposes. Helper functions prepare the amplification curve data for processing as functional data (e.g., Hausdorff distance) or enable the plotting of amplification curve classes (negative, ambiguous, positive). The hookreg() and hookregNL() functions (Burdukiewicz M. et al. (2018) <doi:10.1016/j.bdq.2018.08.001>) can be used to predict amplification curves with an hook effect-like curvature. The pcrfit_single() function can be used to extract features from an amplification curve.
bcp, changepoint, chipPCR, ecp, fda.usc, MBmca, pbapply,
pracma, qpcR, robustbase, shiny, stats, utils, zoo
DT, FFTrees, knitr, RDML, randomForest, readxl, rmarkdown,
spelling, testthat, xtable
Stefan Roediger [cre, aut] (<https://orcid.org/0000-0002-1441-6512>),
Michal Burdukiewicz [aut] (<https://orcid.org/0000-0001-8926-582X>),
Andrej-Nikolai Spiess [aut],
Konstantin A. Blagodatskikh [aut],
Dominik Rafacz [ctb]

DT, FFTrees, knitr, RDML, randomForest, readxl, rmarkdown,
spelling, testthat, xtable, future, listenv

c(person("Stefan", "Roediger",
email = "stefan.roediger@b-tu.de",
comment = c(ORCID = "0000-0002-1441-6512"),
role = c("cre", "aut")),
person("Michal", "Burdukiewicz",
email = "michalburdukiewicz@gmail.com",
comment = c(ORCID = "0000-0001-8926-582X"),
role = c("aut")),
person("Andrej-Nikolai", "Spiess",
email = "a.spiess@uke.de",
comment = c(ORCID = "0000-0002-9630-4724"),
role = c("aut")),
person("Konstantin A.", "Blagodatskikh",
email = "k.blag@yandex.ru",
comment = c(ORCID = "0000-0002-8732-0300"),
role = c("aut")),
person("Dominik", "Rafacz",
email = "dominikrafacz@gmail.com",
comment = c(ORCID = "0000-0003-0925-1909"),
role = c("ctb")))
Extracts features from amplification curve data of quantitative
Polymerase Chain Reactions (qPCR) (Pabinger S. et al. (2014)
<doi:10.1016/j.bdq.2014.08.002>) for machine learning purposes. Helper
functions prepare the amplification curve data for processing as functional
data (e.g., Hausdorff distance) or enable the plotting of amplification
curve classes (negative, ambiguous, positive). The hookreg() and hookregNL()
functions (Burdukiewicz M. et al. (2018) <doi:10.1016/j.bdq.2018.08.001>)
can be used to predict amplification curves with an hook effect-like
curvature. The pcrfit_single() function can be used to extract features
from an amplification curve.
bcp, changepoint, chipPCR, ecp, fda.usc, MBmca, pbapply,
pracma, qpcR, robustbase, segmented, shiny, stats, utils, zoo
DT, future, knitr, listenv, RDML, readxl, rmarkdown,
shinycssloaders, spelling, testthat, xtable
Stefan Roediger [cre, aut] (<https://orcid.org/0000-0002-1441-6512>),
Michal Burdukiewicz [aut] (<https://orcid.org/0000-0001-8926-582X>),
Andrej-Nikolai Spiess [aut] (<https://orcid.org/0000-0002-9630-4724>),
Konstantin A. Blagodatskikh [aut]
(<https://orcid.org/0000-0002-8732-0300>),
Dominik Rafacz [ctb] (<https://orcid.org/0000-0003-0925-1909>)