curatedPCaData

[Syksy]

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[bioc-issue-bot]

Hi @Syksy

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: curatedPCaData
Title: Curated Prostate Cancer Data
Version: 0.99.0
Date: 2023-05-18
Authors@R: c(person("Teemu Daniel", "Laajala", email = "teelaa@utu.fi", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7016-7354")),
   person("Jordan", "Creed", email = "jordan.h.creed@moffitt.org", role = "ctb"),
   person("Christelle", "Colin Leitzinger", email = "christelle.colinleitzinger@moffitt.org", role = "ctb"),
   person("Varsha", "Sreekanth", email = "varsha.sreekanth@cuanschutz.edu", role = "ctb"), 
   person("Federico", "Calboli", email = "federico.calboli@utu.fi", role = "ctb"),
   person("Kalaimathy", "Singaravelu", email = "kalsin@utu.fi", role = "ctb"),
   person("Michael", "Orman", email = "michael.orman@cuanschutz.edu", role = "ctb"),
   person("Alex", "Soupir", email = "Alex.Soupir@moffitt.org", role = "ctb"),
   person("Anni", "Halkola", email = "ansuha@utu.fi", role = "ctb")
 )
Description: The package curatedPCaData offers a selection of annotated prostate cancer datasets featuring multiple omics, manually curated metadata, and derived downstream variables. The studies are offered as MultiAssayExperiment (MAE) objects via ExperimentHub, and comprise of clinical characteristics tied to gene expression, copy number alteration and somatic mutation data. Further, downstream features computed from these multi-omics data are offered. Multiple vignettes help grasp characteristics of the various studies and provide example exploratory and meta-analysis of leveraging the multiple studies provided here-in.
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.2.3
VignetteBuilder: knitr
biocViews: ExperimentHub, ExperimentData, ProstateCancerData, CancerData, Homo_sapiens_Data, MicroarrayData, RNASeqData, ExpressionData, CopyNumberVariationData, Somatic, GEO, ReproducibleResearch
URL: https://github.com/Syksy/curatedPCaData
BugReports: https://github.com/Syksy/curatedPCaData/issues
Depends: R (>= 4.3.0), S4Vectors (>= 0.23.18), MultiAssayExperiment, RaggedExperiment 
Imports: 
    ExperimentHub,
    AnnotationHub,
    utils, 
    methods,
    rlang,
    stats,
    stringr,
    dplyr,
    testthat    
Suggests: 
    BiocGenerics,
    knitr,
    ggplot2,
    rmarkdown,
    survival,
    survminer,
    ComplexHeatmap,
    corrplot    

[bioc-issue-bot]

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[bioc-issue-bot]

Dear Package contributor,

This is the automated single package builder at bioconductor.org.

Your package has been built on Linux, Mac, and Windows.

On one or more platforms, the build results were: "ERROR". This may mean there is a problem with the package that you need to fix. Or it may mean that there is a problem with the build system itself.

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/curatedPCaData to trigger a new build. A quick tutorial for setting up remotes and pushing to upstream can be found here.

[Syksy]

Dear @LiNk-NY ,

thank you for reviewing our package submission!

In its current state, the package runs but produces one ERROR on the 'nebbiolo2 CHECK output'-platform for unit tests. I am checking out the ERROR reported by the bot above, and it is in:

* checking for unstated dependencies in tests ... OK
* checking tests ...
  Running runTests.R [10s/10s]
 [10s/10s] ERROR
Running the tests in tests/runTests.R failed.
Last 20 lines of output:

  Welcome to Bioconductor

      Vignettes contain introductory material; view with
      'browseVignettes()'. To cite Bioconductor, see
      'citation("Biobase")', and for packages 'citation("pkgname")'.

  Attaching package: 'Biobase'

  The following object is masked from 'package:MatrixGenerics':

      rowMedians

  The following objects are masked from 'package:matrixStats':

      anyMissing, rowMedians

  Error in library("RUnit", quietly = TRUE) : 
    there is no package called 'RUnit'
  Calls: <Anonymous> -> library
  Execution halted

It appears that I've misunderstood the notation, and was accidentally mixing RUnit and testthat unit test notation, while my original intention was to only use the latter for unit tests. I will correct this mistake and bump it as soon as I'm done testing it on my own system. My apologies as I was not able to produce this error, as I had both unit test packages available.

[bioc-issue-bot]

Received a valid push on git.bioconductor.org; starting a build for commit id: 53937bb522532fb372bf792758ba4da56033606b

[bioc-issue-bot]

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/curatedPCaData to trigger a new build. A quick tutorial for setting up remotes and pushing to upstream can be found here.

[LiNk-NY]

Hi @Syksy Thank you for your submission. Please see the review below. Best, Marcel

---

curatedPCaData #3047

• Please separate the analysis functionality from the infrastructure. The package should only deliver the data to the user and demonstrate how to obtain the data from ExperimentHub.
• The package has extensive efforts to work with the metadata but these can be avoided by strategically using metadata from the ExperimentHub resources.

DESCRIPTION

• Looks good.
• The BiocGenerics Suggests: seems out of place.
• The CC license is not good for software consider using a different license.

NAMESPACE

• Avoid importing testthat and the expect_s4_class function. To test for S4 class, use the name of the class, e.g., is(X, "KnownS4Class")

vignettes

• Consider restricting the text to 80 column width. It makes it easier to review and maintain.
• The demos in the vignettes should show the user how to obtain the data, it does not necessarily need to show the user how to analyze it. The latter would be more appropriate in a separate workflow package.
• Remove the analysis vignette as it is out of the scope of the package.
• Consider using MatchedAssayExperiment instead of intersecting colnames and "sample_names" in the colData (Decipher vs BCR section).
• Consider using $ to access colData columns, e.g., mae_ren$sample_type
• Consider using TCGAutils::TCGAprimaryTumors for subsetting for primary tumor samples with TCGA data.
• The Oncoprint example looks complicated for a new user. If you are working with TCGA data consider using TCGAutils::oncoPrintTCGA, otherwise consider adding a helper function.
• Reduce the amount of code in the overview.Rmd vignette. Pre-processing with eval(parse(...)) is usually not the way to go. It seems that more needs to be done at the dataset and package level to avoid the juggling of metadata in the vignette. Add helper functions in the package to extract and create a single row for each of your summaries of interests, for example, gleasons(mae_object) would return one row as given by the pre-processing step in the vignette.
• Consider using *.bib reference file rather than hard-coding references in the vignette.
• Use single backticks to enclose code nouns, e.g. colData and MultiAssayExperiment

R

• Use a more descriptive argument than slots in getPCa
• Consider refactoring the logic in wrapperSortonco. It may be safer and more robust to apply a function recursively instead.

tests

• Consider increasing the coverage:

  > covr::package_coverage()
  curatedPCaData Coverage: 24.14%
  R/wrappers.R: 0.00%
  R/getpca.R: 76.92%

[Syksy]

Hi @LiNk-NY ,

thank you for a very throughout review of the package; the points raised make sense, and I am in the process of addressing them.

There were however couple items for which I would like to inquire more information and advice; these mainly stem from the fact that the package is associated to a publication that is already accepted (the journal in question is Scientific Data), and is in the editorial phase:

The CC license is not good for software consider using a different license.

I do agree with this sentiment, and would normally use MIT or GPL-3; however, the editor of the journal requested here prior to acceptance (citing verbatim, the package was previously GPL-3 license): "* Please issue your code with a CC0 or CC-BY licence. The current GNU GPL is too restrictive to comply with Scientific Data's publication policies."

And we've already complied with the above request. Would it be possible to continue having the current CC-BY 4.0 license, as this appears to be a requirement for this particular journal? To my understanding, it would be acceptable for a software package focusing on data rather than code to use the CC licenses.

Please separate the analysis functionality from the infrastructure. The package should only deliver the data to the user and demonstrate how to obtain the data from ExperimentHub.

I understand your point and see that for a package labeled ExperimentData this makes sense, thus we're pursuing this. However, for the publication, we did already create a lot of vignette material which reproduced perfectly the conclusions and even figures of the publication, and this was part of the appeal as the reader could easily track down how the resources were utilized.

Is there some way you could suggest that we could keep vignettes? My first intuition is to separate these into an another package, call it for example curatedPCaAnalysis, and keep linking to its GitHub in the current package for user/reader convenience. All the computational portions as well as downstream inference would be then still easily accessible, while for this package we'd focus solely on the data and its retrieval as requested here.

My plan would be that we would then later return to this analysis-focused package and see if it'd be Bioconductor-compatible or not, after the curatedPCaData has been revised according to the requested changes.

Thank you for any insight or advice!

[LiNk-NY]

Hi @Syksy

And we've already complied with the above request. Would it be possible to continue having the current CC-BY 4.0 license, as this appears to be a requirement for this particular journal? To my understanding, it would be acceptable for a software package focusing on data rather than code to use the CC licenses.

I am no authority in licensing but it seems that we do have some packages using the CC license already.

However, for the publication, we did already create a lot of vignette material which reproduced perfectly the conclusions and even figures of the publication, and this was part of the appeal as the reader could easily track down how the resources were utilized.

The vignette materials belong in a separate repository. The data and its analysis should be separate entities and of course the latter depends on the former. Consider creating your own workflow package (which would be a compilation of your vignette material) that depends on the experiment data package.

Is there some way you could suggest that we could keep vignettes? My first intuition is to separate these into an another package, call it for example curatedPCaAnalysis, and keep linking to its GitHub in the current package for user/reader convenience. All the computational portions as well as downstream inference would be then still easily accessible, while for this package we'd focus solely on the data and its retrieval as requested here.

Yes, the curatedPCaAnalysis idea relates to my response above and it would work well to separate data and analyses. It could be achieved in package format where curatedPCaData is a listed dependency in the DESCRIPTION file.

Note that the vignettes in the current package should only demonstrate to the user how to obtain the data and refer users to curatedPCaAnalysis for example analysis workflows using the data.

[bioc-issue-bot]

Received a valid push on git.bioconductor.org; starting a build for commit id: de225bb237a958dbaf822602408cf477e5fc0db2

[bioc-issue-bot]

Dear Package contributor,

This is the automated single package builder at bioconductor.org.

Your package has been built on the Bioconductor Build System.

On one or more platforms, the build results were: "ERROR, skipped". This may mean there is a problem with the package that you need to fix. Or it may mean that there is a problem with the build system itself.

Please see the build report for more details.

The following are build products from R CMD build on the Bioconductor Build System: ERROR before build products produced.

Links above 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/curatedPCaData to trigger a new build. A quick tutorial for setting up remotes and pushing to upstream can be found here.

[Syksy]

Above ERROR resulted from the fact that the build service uses R 4.3.1, while BiocCheck had suggested that I elevate the package's R dependency to R 4.4.0. I have now reverted back the dependency to R version 4.3.0.

[bioc-issue-bot]

Received a valid push on git.bioconductor.org; starting a build for commit id: dc4bdc2415d75312999a5d10a986720068f77100

[bioc-issue-bot]

Dear Package contributor,

This is the automated single package builder at bioconductor.org.

Your package has been built on the Bioconductor Build System.

Congratulations! The package built without errors or warnings on all platforms.

Please see the build report for more details.

The following are build products from R CMD build on the Bioconductor Build System: Linux (Ubuntu 22.04.2 LTS): curatedPCaData_0.99.3.tar.gz macOS 12.6.5 Monterey: curatedPCaData_0.99.3.tar.gz

Links above 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/curatedPCaData to trigger a new build. A quick tutorial for setting up remotes and pushing to upstream can be found here.

[Syksy]

Hello @LiNk-NY

Thank you once again for taking the time to review our package submission.

As you will find above, the 0.99.3 submission check passes (with my little blunder on too high R 4.4 requirement on 0.99.2, apologies about that).

I have compiled a point-by-point list of your original review citing it verbatim at: https://github.com/Syksy/curatedPCaData/issues/44

At each check-box you'll find your original comments, with the response and corresponding links below each point. I have tried my best to directly list commits and other practical changes that have now been implemented to the package based on your review comments. I hope you will find these improvements satisfactory, making curatedPCaData suitable to be a Bioconductor-package of this type.

[LiNk-NY]

Hi @Syksy Sorry for the delay. Thank you for making those changes. Can you bump the version for another build? Can you please post the list in issue https://github.com/Syksy/curatedPCaData/issues/44 as a comment on this thread? Thank you. Best, Marcel

[bioc-issue-bot]

Received a valid push on git.bioconductor.org; starting a build for commit id: ae20f852e4a8612eb76830039924181ce10fd2c1

[bioc-issue-bot]

Dear Package contributor,

This is the automated single package builder at bioconductor.org.

Your package has been built on the Bioconductor Build System.

Congratulations! The package built without errors or warnings on all platforms.

Please see the build report for more details.

The following are build products from R CMD build on the Bioconductor Build System: Linux (Ubuntu 22.04.2 LTS): curatedPCaData_0.99.4.tar.gz macOS 12.6.5 Monterey: curatedPCaData_0.99.4.tar.gz

Links above 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/curatedPCaData to trigger a new build. A quick tutorial for setting up remotes and pushing to upstream can be found here.

[Syksy]

Hello @LiNk-NY thank you, much appreciated! Above is the latest version bump reported by the bot, which passed cleanly. Here is the original point-by-point response provided for the review (unedited from Aug 10th):

curatedPCaData https://github.com/Bioconductor/Contributions/issues/3047

• [x] Please separate the analysis functionality from the infrastructure. The package should only deliver the data to the user and demonstrate how to obtain the data from ExperimentHub.

OK; As discussed in the submission thread for BioConductor at https://github.com/Bioconductor/Contributions/issues/3047#issuecomment-1625486043 , these downstream vignettes are to be moved to a new workflow package called curatedPCaWorkflow at: https://github.com/Syksy/curatedPCaWorkflow . Work will continue there-in regarding the proposed improvements. Please see answers below for specific actions taken toward this package-wide adjustment.

• [x] The package has extensive efforts to work with the metadata but these can be avoided by strategically using metadata from the ExperimentHub resources.

Multiple steps have been taken to address this and are further elaborated in answers below; briefly:

• Functionality that previously existed inside the main overview vignette has been moved to /R/getpcasummaries.R, and these functions are generalizable to any requested variable, are not dataset-specific, and contain e.g. standard documentation and unit testing.
• Functionality that was deemed to be more toward downstream analyses has been moved away from the package, in particular functions contained previously at /R/wrappers.R.
• The new functions utilize strategically both the internally provided metadata.csv (for example function getPCaStudies) as well as the clinical metadata accessible via colData (for example functions getPCaSummaryTable, getPCaSummarySurv, getPCaSummarySamples , and getPCaSummaryStudies).

DESCRIPTION

• [x] Looks good.

OK

• [x] The BiocGenerics Suggests: seems out of place.

OK; Addressed in https://github.com/Syksy/curatedPCaData/commit/7ba59f5fb1bd917a0a0fc48e5245aad34cfec51b ; in addition, the package dependencies have been made more lean as the functions at /R/wrappers.R are no longer present, removing the import need for e.g. dplyr and stringr.

• [x] The CC license is not good for software consider using a different license.

OK; CC-BY 4.0 should still be fine based on discussion at https://github.com/Bioconductor/Contributions/issues/3047#issuecomment-1625486043

NAMESPACE

• [x] _Avoid importing testthat and the expect_s4class function. To test for S4 class, use the name of the class, e.g., is(X, "KnownS4Class")

OK; This has now been adjusted to using just native R tests in: https://github.com/Syksy/curatedPCaData/commit/4c1a481a82060d371da958c23980853ac0080ed5 and the tests themselves were subsequently extended to cover the new functions at https://github.com/Syksy/curatedPCaData/commit/32352235fd9f0df8c04fd5d677ae2fb53c2b984c and https://github.com/Syksy/curatedPCaData/commit/ed9d0b8b2b0fd1ab2c8fd7647224fa05a062caa8 .

vignettes

• [x] Consider restricting the text to 80 column width. It makes it easier to review and maintain.

OK; this formatting issue has been addressed in https://github.com/Syksy/curatedPCaData/commit/bb76b0121fdd8fcd7e949c583160eb36d1c04db4 . Furthermore, the rest of the code has also been double-checked for formatting (multiples of 4 spacebar indents, no tabs, char width 80), of which vast majority are fixed in e.g. https://github.com/Syksy/curatedPCaData/commit/019a304e9dffaf0b92046a308486dd80b6b5eec8 . The 12 lines (~0% of all code) longer than 80 chars and 11 lines (~0% of all code) that violate 4*spacebar multiples are special cases such as long URLs or automatically generated package Rd TeX-like code, respectively.

• [x] The demos in the vignettes should show the user how to obtain the data, it does not necessarily need to show the user how to analyze it. The latter would be more appropriate in a separate workflow package.

OK; Notable effort has been put into revising the overview vignette so it exemplifies this effectively with generalizable code, while rest of the vignettes that had an analysis focus have been now shifted to the separate curatedPCaWorkflow-package. See comments below.

• [x] Remove the analysis vignette as it is out of the scope of the package.

OK; Done in https://github.com/Syksy/curatedPCaData/commit/ee1c94bd8e09d9e6d072e5c5f27dd1655bcbc872 by moving to use curatedPCaWorkflow instead.

• [x] _Consider using MatchedAssayExperiment instead of intersecting colnames and "samplenames" in the colData (Decipher vs BCR section).

OK; This pointer has been addressed in the PR by @ACSoupir at https://github.com/Syksy/curatedPCaData/pull/45 . However, as the analyses vignette has now been incorporated into the workflow package instead, this fix was not merged into curatedPCaData. This fix will instead be incorporated into the curatedPCaWorkflow.

• [x] _Consider using $ to access colData columns, e.g., mae_ren$sampletype

OK; After the other fixes both into the overview vignette as well as the generalized functions provided now in /R/getpcasummaries.R, all calls of type mae_obj[,"variable"] are now instead formatted as mae_obj$variable.

• [x] Consider using TCGAutils::TCGAprimaryTumors for subsetting for primary tumor samples with TCGA data.

OK, at least partially; this revision request was not implemented as suggested here per se. I explored both the TCGAutils (and the curatedTCGAdata) for this purpose. However, I ended up with the conclusion that this would've been best implemented as part of the data curation step in curatedPCaData, rather than implemented here when we're just offering the "window" to the data. In all of our datasets, the sample_type field in colData was extracted from the original source and represents the sample types (primary, normal, metastatic, ...). In this respect, I feel it would be a bit much to bring in a dependency just for TCGA, when this information is already available in the colData. However, to address the original purpose of subsetting to certain sample types, I have now modified the main getPCa function to allow subsetting the creation of the MAE object via MultiAssayExperiment::subsetByColData which will utilize the sample_type available in our data : https://github.com/Syksy/curatedPCaData/commit/ba50efa4e16d266593589403709ae6a4ffa9f9cf . I hope this adjustment fulfills the requested improvement to the package - if not, please let me know and I will revise accordingly to the best of my ability.

• [x] The Oncoprint example looks complicated for a new user. If you are working with TCGA data consider using TCGAutils::oncoPrintTCGA, otherwise consider adding a helper function.

OK; The oncoprint related functions and wrappers have been now moved to the new curatedPCaWorkflow package (e.g. commit https://github.com/Syksy/curatedPCaData/commit/62be8a42449421dac6888d1d3c787242be8ea192 ). In hindsight, they are somewhat downstream analyses already, and therefore are better suited for this workflow-package. Thus, the oncoprint and their related functions are no longer part of the curatedPCaData-package.

• [x] _Reduce the amount of code in the overview.Rmd vignette. Pre-processing with eval(parse(...)) is usually not the way to go. It seems that more needs to be done at the dataset and package level to avoid the juggling of metadata in the vignette. Add helper functions in the package to extract and create a single row for each of your summaries of interests, for example, gleasons(maeobject) would return one row as given by the pre-processing step in the vignette.

OK; This is a fair point, and indeed should've been the design already in the start. Notable effort has now been done to move the functionality previously coded inside the overview vignette, and made exported by the package itself accompanied by suitable examples, testing, and documentation. To enumerate these functions and key commits, of which majority resides now in the file located at R/getpcasummaries.R:

• getPCaSummaryTable : https://github.com/Syksy/curatedPCaData/commit/6d1aaaae356f11c1cdc73715aba9540e353ee2cc
• getPCaSummarySurv : https://github.com/Syksy/curatedPCaData/commit/6d1aaaae356f11c1cdc73715aba9540e353ee2cc
• getPCaSummarySamples : https://github.com/Syksy/curatedPCaData/commit/e4129376e1b6280bf14483baf908e51852bdfc43
• getPCaSummaryStudies : https://github.com/Syksy/curatedPCaData/commit/b6bffd1ef4b3e669583576e9acc256d5e9934d84 and https://github.com/Syksy/curatedPCaData/commit/f8ef61ce7aec3e6da965afdbe65b5a5b46b19bea
• getPCaStudies : https://github.com/Syksy/curatedPCaData/commit/71c73c6cc4b44401c730d9e7f268f8a9942fcf0c

With the above functions now in use by the vignette and exported by the user, the revision(s) addresses the above mentioned issues and clean up the vignette (Rmd commits mainly in https://github.com/Syksy/curatedPCaData/commit/2f1b02a34948400e99cf18c88b5053b3b6b81981 , https://github.com/Syksy/curatedPCaData/commit/90a37f3ba3609a4870da170c8fc375bcaf475a16 , https://github.com/Syksy/curatedPCaData/commit/7abab8cab289c3f31e587341874ae6121d4a248b )

• [x] Consider using *.bib reference file rather than hard-coding references in the vignette.

OK; The citations have now been revised as requested in https://github.com/Syksy/curatedPCaData/commit/1940b500d809ac06046f120d280bb9f2de49731a and https://github.com/Syksy/curatedPCaData/commit/dff5dee69bc1506bb6485913bea998d43db1ad86 .

• [x] Use single backticks to enclose code nouns, e.g. colData and MultiAssayExperiment

OK; These mentioned triple backticks have been corrected to single backticks in https://github.com/Syksy/curatedPCaData/commit/0b46706998a9b783c023dcff28e79fa10f3045ea .

R

• [x] Use a more descriptive argument than slots in getPCa

OK; The name of the argument has now been changed to assays in order to be more in line with the naming conventions in MultiAssayExperiment via commit https://github.com/Syksy/curatedPCaData/commit/633d17eb510a5d9c1375b88901af409d20fe718f .

• [x] Consider refactoring the logic in wrapperSortonco. It may be safer and more robust to apply a function recursively instead.

OK; This function has now been moved to be as part of the curatedPCaWorkflow instead, as it relates to oncoprints ( https://github.com/Syksy/curatedPCaData/commit/62be8a42449421dac6888d1d3c787242be8ea192 ).

tests

• [x] Consider increasing the coverage:

  covr::package_coverage() curatedPCaData Coverage: 24.14% R/wrappers.R: 0.00% R/getpca.R: 76.92%

After adjustments such as moving the wrappers focused on e.g. oncoprints out of the package ( https://github.com/Syksy/curatedPCaData/commit/62be8a42449421dac6888d1d3c787242be8ea192 ), adding new functions for replacing the ones previously defined inside vignette (e.g. https://github.com/Syksy/curatedPCaData/commit/6d1aaaae356f11c1cdc73715aba9540e353ee2cc , https://github.com/Syksy/curatedPCaData/commit/e4129376e1b6280bf14483baf908e51852bdfc43 , https://github.com/Syksy/curatedPCaData/commit/b6bffd1ef4b3e669583576e9acc256d5e9934d84 , https://github.com/Syksy/curatedPCaData/commit/71c73c6cc4b44401c730d9e7f268f8a9942fcf0c), and extending the testing coverage ( https://github.com/Syksy/curatedPCaData/commit/32352235fd9f0df8c04fd5d677ae2fb53c2b984c ), the output for this coverage testing is as follows:

covr::package_coverage() curatedPCaData Coverage: 89.60% R/getpca.R: 79.61% R/getpcasummaries.R: 94.87%

Thus, key functionality is tested, with the parts not covered related to marginal cases like download timeout.

[Syksy]

Hi @Syksy Sorry for the delay. Thank you for making those changes. Can you bump the version for another build? Can you please post the list in issue Syksy/curatedPCaData#44 as a comment on this thread? Thank you. Best, Marcel

Dear @LiNk-NY Thank you once again for taking a comprehensive look at the package - however, would it be possible to have this review round some time soon? We had originally hoped to aim for the Bioconductor 3.18, and the window seems to be closing. Apologies for the extra poke!

[LiNk-NY]

Apologies for the delay @Syksy

The release has been a handful to say the least. Thank you for making those changes. The vignette looks to be in good shape. I noticed that you are running getPCa on all available studies. Although there is caching implemented, I think it would be more natural for the user to see an example with a single study or smaller subset of studies, e.g., getPCa('abida') and show how the package works with those.

Note. I currently can't do

maes <- lapply(studies[1:2], getPCa)
studytable <- getPCaSummaryStudies(maes)

Best regards, Marcel

[LiNk-NY]

@Syksy Any updates? Thanks!

[bioc-issue-bot]

Received a valid push on git.bioconductor.org; starting a build for commit id: 3e9f631e7e1a99fd16a1d26237cef1fa18b450a7

[bioc-issue-bot]

Dear Package contributor,

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Your package has been built on the Bioconductor Build System.

Congratulations! The package built without errors or warnings on all platforms.

Please see the build report for more details.

The following are build products from R CMD build on the Bioconductor Build System: macOS 12.6.5 Monterey: curatedPCaData_0.99.5.tar.gz Linux (Ubuntu 22.04.2 LTS): curatedPCaData_0.99.5.tar.gz

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[Syksy]

Dear @LiNk-NY , thank you for your patience - I hope to have now addressed the remaining issues, and you can find the latest curatedPCaData v0.99.5 build above by the bot.

The release has been a handful to say the least. Thank you for making those changes. The vignette looks to be in good shape. I noticed that you are running getPCa on all available studies. Although there is caching implemented, I think it would be more natural for the user to see an example with a single study or smaller subset of studies, e.g., getPCa('abida') and show how the package works with those.

Thanks, the vignette has now been refined further; in order to address the practical show-casing of a single study and how to handle the MAE-object, I've added a new section mainly with this commit: https://github.com/Syksy/curatedPCaData/commit/14dd8d269d7317a6588e76dbd96b0b664568f961

I did not include the abida study as it consists of metastatic samples only, and is therefore a very specialized study. Instead, I picked the representative taylor study. As you can see from the overview vignette's new Section 5, this latest addition showcases retrieval of data, its subsets (both in assays and sample_types), and then proceeds to standard survival related analyses such as Kaplan-Meier curves and a Cox proportional hazards model. These were part of readily suggested survival and survminer, so no further Suggests were necessary to add this. At the same time, this new section briefly highlights such useful functions as wideFormat and longFormat from MultiAssayExperiment for their importance to end-users.

Hopefully you'll find this use-case useful in respect to the request above.

Note. I currently can't do

maes <- lapply(studies[1:2], getPCa)
studytable <- getPCaSummaryStudies(maes)

This was a mistake on my part, with building the getPCaSummaryStudies function with the implicit assumption that it is either given a named list or a character vector of study names. In this cited case the function was caught in an error, as a non-named list is given instead. This issue has now been addressed in the following commit: https://github.com/Syksy/curatedPCaData/commit/b78ccff4450b1b65407b67bef368ac67b1d369a0 After this adjustment, above issue is resolved.

Further small commits are also added, mainly with minimal fixes or e.g. code formatting.

I hope that you would find above adjustments suitable for these requested revisions, Kind regards, Syksy

[LiNk-NY]

Hi Syksy, @Syksy Thank you for making those changes. Your package has been accepted. Best regards, Marcel

[bioc-issue-bot]

Your package has been accepted. It will be added to the Bioconductor nightly builds.

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