Closed ElkeDebrie closed 2 years ago
Hi @ElkeDebrie
Thanks for submitting your package. We are taking a quick look at it and you will hear back from us soon.
The DESCRIPTION file for this package is:
Package: TargetDecoy
Title: Diagnostic Plots to Evaluate the Target Decoy Approach
Version: 0.99.3
Date: 2021-09-10
Authors@R: c(
person(given = "Elke",
family = "Debrie",
role = c("aut", "cre"),
email = "elkedebrie@gmail.com"),
person(given = "Lieven",
family = "Clement",
role = c("aut"),
email = "lieven.clement@ugent.be",
comment = c(ORCID = "0000-0002-9050-4370")),
person(given = "Milan",
family = "Malfait",
role = "ctb",
email = "milan.malfait@ugent.be",
comment = c(ORCID = "0000-0001-9144-3701"))
)
Description:
A first step in the data analysis of Mass Spectrometry (MS) based proteomics
data is to identify peptides and proteins. With this respect the huge number of
experimental mass spectra typically have to be assigned to theoretical peptides
derived from a sequence database. Search engines are used for this purpose.
These tools compare each of the observed spectra to all candidate theoretical
spectra derived from the sequence data base and calculate a score for each
comparison. The observed spectrum is then assigned to the theoretical peptide
with the best score, which is also referred to as the peptide to spectrum match
(PSM). It is of course crucial for the downstream analysis to evaluate the
quality of these matches. Therefore False Discovery Rate (FDR) control is used
to return a reliable list PSMs. The FDR, however, requires a good
characterisation of the score distribution of PSMs that are matched to the wrong
peptide (bad target hits). In proteomics, the target decoy approach (TDA) is
typically used for this purpose. The TDA method matches the spectra to a
database of real (targets) and nonsense peptides (decoys). A popular approach to
generate these decoys is to reverse the target database. Hence, all the PSMs
that match to a decoy are known to be bad hits and the distribution of their
scores are used to estimate the distribution of the bad scoring target PSMs. A
crucial assumption of the TDA is that the decoy PSM hits have similar properties
as bad target hits so that the decoy PSM scores are a good simulation of the
target PSM scores. Users, however, typically do not evaluate these assumptions.
To this end we developed TargetDecoy to generate diagnostic plots to
evaluate the quality of the target decoy method.
License: Artistic-2.0
URL: https://github.com/statOmics/TargetDecoy
BugReports: https://github.com/statOmics/TargetDecoy/issues
biocViews: MassSpectrometry, Proteomics, QualityControl, Software, Visualization
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.1.2
Depends: R (>= 4.1)
Imports:
ggplot2,
ggpubr,
methods,
mzID,
mzR,
shiny,
stats
Suggests:
BiocStyle,
knitr,
msdata,
sessioninfo,
rmarkdown,
gridExtra,
testthat (>= 3.0.0),
covr
VignetteBuilder: knitr
Config/testthat/edition: 3
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Received a valid push on git.bioconductor.org; starting a build for commit id: 95f411c73a3553886e9d1d2db1e892ece78d1a51
Dear Package contributor,
This is the automated single package builder at bioconductor.org.
Your package has been built on Linux, Mac, and Windows.
Congratulations! The package built without errors or warnings on all platforms.
Please see the build report for more details. This link will be active for 21 days.
Remember: if you submitted your package after July 7th, 2020,
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Hi @ElkeDebrie
Sorry for the delay. Thank you for your submission. Please see the review below.
Best regards, Marcel
time R CMD build TargetDecoy
.grid.arrange
or
perhaps have one function return one plot?Sys.time
sections are not needed and could be removed.log10 = TRUE
in the body
and test for deviations e.g., stopifnot(is.logical(log10))
etc. rather
than testing if missing
or NULL
.decoy
and score
, e.g.,
stop("Column(s) not found:", ...)
in app.R
.check_args
.log10
is consistent throught your
functions app.R
vs evalTargetDecoys()
.> covr::package_coverage()
TargetDecoy Coverage: 57.41%
R/app.R: 15.95%
R/Scores.R: 97.92%
R/createPPlotObjects.R: 100.00%
R/evalTargetDecoys.R: 100.00%
R/plots.R: 100.00%
R/utils.R: 100.00%
Received a valid push on git.bioconductor.org; starting a build for commit id: d2d83d23d4247c99a4845cdde3a04570f0aea8f6
Hi @LiNk-NY
Thanks for the review and the feedback. I addressed your comments. The shiny app has been removed and can later be given as a separate package.
Best regards Elke
Dear Package contributor,
This is the automated single package builder at bioconductor.org.
Your package has been built on Linux, Mac, and Windows.
Congratulations! The package built without errors or warnings on all platforms.
Please see the build report for more details. This link will be active for 21 days.
Remember: if you submitted your package after July 7th, 2020,
when making changes to your repository push to
git@git.bioconductor.org:packages/TargetDecoy
to trigger a new build.
A quick tutorial for setting up remotes and pushing to upstream can be found here.
Hi Elke, @ElkeDebrie Please respond to each point in the review. Thank you. Best, Marcel
Hi Marcel, @LiNk-NY Thanks for your reply. See attached the comments to each point. RespondReview.pdf
Hi Elke, @ElkeDebrie Thank you for your prompt response. Please respond as an inline comment by clicking on the dots on the top right and hitting "quote reply" and do not attach files next time. Your package has been accepted. Thank you for your contribution. Best regards, Marcel
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