Date accepted: 2022-06-10
Submitting Author Name: Edward Wallace
Submitting Author Github Handle: @ewallace
Other Package Authors Github handles: @DimmestP
Repository: https://github.com/ewallace/tidyqpcr
Version submitted: 0.3.0
Submission type: Standard
Editor: @jooolia
Reviewers: @kelshmo
Due date for
@kelshmo: 2021-11-19
Archive: TBD
Version accepted: TBD
- Paste the full DESCRIPTION file inside a code block below:
Package: tidyqpcr
Type: Package
Title: Quantitative PCR Analysis with the Tidyverse
Version: 0.3
Authors@R: c(person("Edward", "Wallace", email = "Edward.Wallace@ed.ac.uk", role = c("aut", "cre")),
person("Sam", "Haynes", email = "samuel.haynes10@gmail.com", role = c("ctb")))
Description: This package is for reproducible quantitative PCR (qPCR) analysis using packages from the tidyverse, notably dplyr and ggplot2. It normalizes (by ddCq), summarizes, and plots pre-calculated Cq data, and plots raw amplification and melt curves from Roche LightCycler machines. It does NOT (yet) calculate Cq data from amplification curves.
Depends:
R (>= 3.1.0),
tibble,
tidyr
Imports:
rlang,
dplyr,
ggplot2,
readr,
forcats,
assertthat,
lifecycle
Suggests: knitr,
rmarkdown,
tidyverse,
cowplot,
testthat
VignetteBuilder: knitr
License: Apache License 2.0 + file LICENSE
LazyData: true
RoxygenNote: 7.1.1
Encoding: UTF-8
URL: https://github.com/ewallace/tidyqpcr, https://ewallace.github.io/tidyqpcr/
BugReports: https://github.com/ewallace/tidyqpcr/issues
Language: en-GB
Scope
-
Please indicate which category or categories from our package fit policies this package falls under: (Please check an appropriate box below. If you are unsure, we suggest you make a pre-submission inquiry.):
-
Explain how and why the package falls under these categories (briefly, 1-2 sentences):
tidyqpcr is an R package that empowers scientists to conduct reproducible, flexible, and best-practice compliant quantitative polymerase chain reaction (qPCR) analysis.
tidyqpcr offers a standardised user interface and structure for qPCR analysis, within the tidyverse paradigm of spreadsheet-like rectangular data frames and generic functions that build up complex analyses in a series of simple steps.
- Who is the target audience and what are scientific applications of this package?
Any molecular biologist or bioinformatician who needs to design or analyse a qPCR experiment.
Quantitative PCR is among the most common techniques in biological and biomedical re-
search, used for the quantification of DNA and RNA.
Standardised and open-source qPCR analysis pipelines will encourage best-practices in the reporting of qPCR results, improve the evaluation of qPCR experiments and ultimately lead to increased confidence in conclusions based on qPCR data.
Some open-source libraries for qPCR analysis are available, notably qpcR (Spiess, 2018) and pcr (Ahmed & Kim, 2018). qpcR is a feature rich qPCR analysis package relying on an object-oriented approach using S4 classes. pcr is a less extensive qPCR analysis package based on the tidyverse suite of generic data-science tools using the paradigm of tidy data (spreadsheet-like rectangular data frames). However, available packages either assume extensive prior R knowledge, overlook best-practices in qPCR experiments, or lack extensive documentation. There remains a need for a qPCR analysis package that integrates with the user-friendly tidyverse, encourages the use of MIQE best-practice compliant experimental design, and provides detailed example analysis pipelines as R vignettes.
We presented tidyqpcr on the rOpenSci discussion page which Stefanie Butland and Sean Hughes kindly responded to. In response to @seaaan ’s comments we improved the vignettes, stuck to a consistent function naming convention and added functionality to calculate ddCq. We intend to add further functionality in future versions including: support for absolute quantification, support for multiple targets per well, and enabling the use of the plater package.
In email correspondence with Stefanie, we believe Julia Gustavsen would be a perfect editor for our project as they reviewed Sean’s plater package. As for reviewers, we think someone with experience in conducting assays for RNA/DNA quantification and normalisation would be of benefit because of the emphasis on experimental design best-practices.
Technical checks
Confirm each of the following by checking the box.
This package:
Publication options
We have included a paper.md file in the repository as per JOSS instructions to authors.
Code of conduct
Date accepted: 2022-06-10
Due date for @kelshmo: 2021-11-19Submitting Author Name: Edward Wallace
Submitting Author Github Handle: @ewallace
Other Package Authors Github handles: @DimmestP
Repository: https://github.com/ewallace/tidyqpcr
Version submitted: 0.3.0
Submission type: Standard
Editor: @jooolia
Reviewers: @kelshmo
Archive: TBD
Version accepted: TBD
Scope
Please indicate which category or categories from our package fit policies this package falls under: (Please check an appropriate box below. If you are unsure, we suggest you make a pre-submission inquiry.):
Explain how and why the package falls under these categories (briefly, 1-2 sentences):
tidyqpcr is an R package that empowers scientists to conduct reproducible, flexible, and best-practice compliant quantitative polymerase chain reaction (qPCR) analysis.
tidyqpcr offers a standardised user interface and structure for qPCR analysis, within the tidyverse paradigm of spreadsheet-like rectangular data frames and generic functions that build up complex analyses in a series of simple steps.
Any molecular biologist or bioinformatician who needs to design or analyse a qPCR experiment.
Quantitative PCR is among the most common techniques in biological and biomedical re-
search, used for the quantification of DNA and RNA.
Standardised and open-source qPCR analysis pipelines will encourage best-practices in the reporting of qPCR results, improve the evaluation of qPCR experiments and ultimately lead to increased confidence in conclusions based on qPCR data.
Some open-source libraries for qPCR analysis are available, notably qpcR (Spiess, 2018) and pcr (Ahmed & Kim, 2018). qpcR is a feature rich qPCR analysis package relying on an object-oriented approach using S4 classes. pcr is a less extensive qPCR analysis package based on the tidyverse suite of generic data-science tools using the paradigm of tidy data (spreadsheet-like rectangular data frames). However, available packages either assume extensive prior R knowledge, overlook best-practices in qPCR experiments, or lack extensive documentation. There remains a need for a qPCR analysis package that integrates with the user-friendly tidyverse, encourages the use of MIQE best-practice compliant experimental design, and provides detailed example analysis pipelines as R vignettes.
(If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?
Not applicable.
If you made a pre-submission inquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted.
We presented tidyqpcr on the rOpenSci discussion page which Stefanie Butland and Sean Hughes kindly responded to. In response to @seaaan ’s comments we improved the vignettes, stuck to a consistent function naming convention and added functionality to calculate ddCq. We intend to add further functionality in future versions including: support for absolute quantification, support for multiple targets per well, and enabling the use of the plater package.
In email correspondence with Stefanie, we believe Julia Gustavsen would be a perfect editor for our project as they reviewed Sean’s plater package. As for reviewers, we think someone with experience in conducting assays for RNA/DNA quantification and normalisation would be of benefit because of the emphasis on experimental design best-practices.
Technical checks
Confirm each of the following by checking the box.
This package:
Publication options
Do you intend for this package to go on CRAN?
Do you intend for this package to go on Bioconductor?
Do you wish to submit an Applications Article about your package to Methods in Ecology and Evolution?
Do you wish to submit an Applications Article about your package to Journal of Open Source Software?
We have included a paper.md file in the repository as per JOSS instructions to authors.
Code of conduct