How to cite the R package pcr

Ahmed, M., & Kim, D. R. (2018). pcr: an R package for quality assessment, analysis and testing of qPCR data. PeerJ, 6(e4473), e4473.

1.

Ahmed M, Kim DR. pcr: an R package for quality assessment, analysis and testing of qPCR data. PeerJ. 2018 Mar 16;6(e4473):e4473.

@ARTICLE{Ahmed2018-dt,
  title     = "pcr: an {R} package for quality assessment, analysis and testing
               of {qPCR} data",
  author    = "Ahmed, Mahmoud and Kim, Deok Ryong",
  abstract  = "Background Real-time quantitative PCR (qPCR) is a broadly used
               technique in the biomedical research. Currently, few different
               analysis models are used to determine the quality of data and to
               quantify the mRNA level across the experimental conditions.
               Methods We developed an R package to implement methods for
               quality assessment, analysis and testing qPCR data for
               statistical significance. Double Delta CT and standard curve
               models were implemented to quantify the relative expression of
               target genes from CT in standard qPCR control-group experiments.
               In addition, calculation of amplification efficiency and curves
               from serial dilution qPCR experiments are used to assess the
               quality of the data. Finally, two-group testing and linear
               models were used to test for significance of the difference in
               expression control groups and conditions of interest. Results
               Using two datasets from qPCR experiments, we applied different
               quality assessment, analysis and statistical testing in the pcr
               package and compared the results to the original published
               articles. The final relative expression values from the
               different models, as well as the intermediary outputs, were
               checked against the expected results in the original papers and
               were found to be accurate and reliable. Conclusion The pcr
               package provides an intuitive and unified interface for its main
               functions to allow biologist to perform all necessary steps of
               qPCR analysis and produce graphs in a uniform way.",
  journal   = "PeerJ",
  publisher = "PeerJ",
  volume    =  6,
  number    = "e4473",
  pages     = "e4473",
  month     =  mar,
  year      =  2018,
  url       = "http://dx.doi.org/10.7717/peerj.4473",
  language  = "en",
  issn      = "2167-8359",
  doi       = "10.7717/peerj.4473"
}

TY  - JOUR
AU  - Ahmed, Mahmoud
AU  - Kim, Deok Ryong
AD  - Department of Biochemistry and Convergence Medical Sciences and Institute
      of Health Sciences, Gyeongsang National University School of Medicine,
      Jinju, Gyeongnam, South Korea
TI  - pcr: an R package for quality assessment, analysis and testing of qPCR
      data
T2  - PeerJ
VL  - 6
IS  - e4473
SP  - e4473
PY  - 2018
DA  - 2018/3/16
PB  - PeerJ
AB  - Background Real-time quantitative PCR (qPCR) is a broadly used technique
      in the biomedical research. Currently, few different analysis models are
      used to determine the quality of data and to quantify the mRNA level
      across the experimental conditions. Methods We developed an R package to
      implement methods for quality assessment, analysis and testing qPCR data
      for statistical significance. Double Delta CT and standard curve models
      were implemented to quantify the relative expression of target genes from
      CT in standard qPCR control-group experiments. In addition, calculation of
      amplification efficiency and curves from serial dilution qPCR experiments
      are used to assess the quality of the data. Finally, two-group testing and
      linear models were used to test for significance of the difference in
      expression control groups and conditions of interest. Results Using two
      datasets from qPCR experiments, we applied different quality assessment,
      analysis and statistical testing in the pcr package and compared the
      results to the original published articles. The final relative expression
      values from the different models, as well as the intermediary outputs,
      were checked against the expected results in the original papers and were
      found to be accurate and reliable. Conclusion The pcr package provides an
      intuitive and unified interface for its main functions to allow biologist
      to perform all necessary steps of qPCR analysis and produce graphs in a
      uniform way.
SN  - 2167-8359
DO  - 10.7717/peerj.4473
UR  - http://dx.doi.org/10.7717/peerj.4473
ER  -