dmGsea

[xuz1]

The R package dmGsea provides gene set enrichment analysis functions for complex Omics data. In many omics datasets, such as Illumina DNA methylation data, genes are often represented by variable numbers of correlated probes, and a single probe may map to multiple genes. This complex data structure poses significant challenges for gene set enrichment analysis. The dmGsea package addresses these challenges by providing both threshold- and ranking-based competitive enrichment analysis methods, effectively accounting for probe correlation and probe number bias. These versatile functions can be applied to a wide range of omics datasets, including DNA methylation, proteomics and gene expression data. The github repository is https://github.com/xuz1/dmGsea

[bioc-issue-bot]

Hi @xuz1

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: dmGsea
Type: Package
Title: Geneset Enrichment Analysis for Complex Omics Data
Version: 0.99.0
Authors@R:
  c(person("Zongli","Xu",email="xuz@niehs.nih.gov",role=c("cre","aut"),comment=c(ORCID="0000-0002-9034-8902")),
   person("Alison","Motsinger-Reif",email="alison.motsinger-reif@nih.gov",role="aut"),
    person("Liang", "Niu", role="aut",email="niulg@ucmail.uc.edu"))
Description: The R package dmGsea offers versatile gene set enrichment analysis tools for complex omics datasets, including DNA methylation, proteomics and gene expression. It addresses challenges like correlated probes, probe number bias, and multi-mapping probes with threshold- and ranking-based methods. The package also features a fast, accurate ranking-based method optimized for gene expression and RNA-seq data.
License: Artistic-2.0
biocViews: GeneSetEnrichment, Pathways,DNAMethylation,Proteomics,
	CopyNumberVariation, GeneExpression, GenomicVariation 
Depends: utils,stats,parallel,Matrix
Suggests: msigdbr, org.Hs.eg.db, org.Mm.eg.db, minfi, knitr, rmarkdown, GO.db
Imports:  dqrng,AnnotationDbi,poolr,BiasedUrn
VignetteBuilder: knitr
Encoding: UTF-8
URL: https://github.com/Bioconductor/dmGsea
BugReports: https://github.com/Bioconductor/dmGsea/issues
git_url: https://git.bioconductor.org/packages/dmGsea
Maintainer: Zongli Xu <xuz@niehs.nih.gov>
NeedsCompilation: no

[lshep]

A few preliminary comments:

Please cache data that is downloaded from the web. See BiocFileCache. This will protect against a website going down or limited interent connections.

Please provide a package level man page so that a naive user that does ?dmGsea to know where to get started.

Your vignette executes code but then when it goes to actually run your provided functions it is in a FALSE and not executed. Your code must be executed at least once to show that your function is functioning correctly. Also none of your code is shown in the compiled vignette when opened in R because you chose a echo=FALSE for all code chunks.

Looking at the code manually, it looks like you show Bioconductor interaction but it parses out the structures. You also don't use SingleCellExperiment which is the standard Bioconductor class for omics representation. We normally require packages to work with Bioconductor structures directly.

You can also look at adding unit tests.

[lshep]

Also it looks at the time of submission our submission template was down... could you please confirm the following by editing each check box to '[x]'

• I understand that by submitting my package to Bioconductor,
  the package source and all review commentary are visible to the
  general public.

• I have read the Bioconductor Package Submission
  instructions. My package is consistent with the Bioconductor
  Package Guidelines.

• I understand Bioconductor Package Naming Policy and acknowledge
  Bioconductor may retain use of package name.

• I understand that a minimum requirement for package acceptance
  is to pass R CMD check and R CMD BiocCheck with no ERROR or WARNINGS.
  Passing these checks does not result in automatic acceptance. The
  package will then undergo a formal review and recommendations for
  acceptance regarding other Bioconductor standards will be addressed.

• My package addresses statistical or bioinformatic issues related
  to the analysis and comprehension of high throughput genomic data.

• I am committed to the long-term maintenance of my package. This
  includes monitoring the support site for issues that users may
  have, subscribing to the bioc-devel mailing list to stay aware
  of developments in the Bioconductor community, responding promptly
  to requests for updates from the Core team in response to changes in
  R or underlying software.

• I understand it is my responsibility to maintain a valid, active
  email in the DESCRIPTION of my package. This email should allow emails
  from noreply@bioconductor.org and BBS-noreply@bioconductor.org to allow
  automatic notifications from the Bioconductor team concerning my package.

• I am familiar with the Bioconductor code of conduct and
  agree to abide by it.

I am familiar with the essential aspects of Bioconductor software
management, including:

• The 'devel' branch for new packages and features.
• The stable 'release' branch, made available every six
  months, for bug fixes.
• Bioconductor version control using Git
  (optionally via GitHub).

For questions/help about the submission process, including questions about
the output of the automatic reports generated by the SPB (Single Package
Builder), please use the #package-submission channel of our Community Slack.
Follow the link on the home page of the Bioconductor website to sign up.

[xuz1]

Thanks for the comments. I have revised the code in utils.R to use the KEGGREST package instead, ensuring that data is not downloaded from the web during runtime. Additionally, I added a package-level manual page (dmGsea.Rd) and updated the vignette to execute most of the code. I have tested the file, and all the code in the file runs correctly; however, running the entire code in the file may take a significant amount of time. Regarding the use of SingleCellExperiment classes, all functions in the package currently operate with simple data frames as input. Incorporating SingleCellExperiment could introduce unnecessary complexity, and I am uncertain how best to integrate its class structure within the package. Let me know if you have any suggestions on this aspect.

[xuz1]

Also it looks at the time of submission our submission template was down... could you please confirm the following by editing each check box to '[x]'

• [ ]
  [X ] I understand that by submitting my package to Bioconductor,
  the package source and all review commentary are visible to the
  general public.
  [ ]
  [X ] I have read the Bioconductor Package Submission
  instructions. My package is consistent with the Bioconductor
  Package Guidelines.
  [ ]
  [X ] I understand Bioconductor Package Naming Policy and acknowledge
  Bioconductor may retain use of package name.
  [ ]
  [X ] I understand that a minimum requirement for package acceptance
  is to pass R CMD check and R CMD BiocCheck with no ERROR or WARNINGS.
  Passing these checks does not result in automatic acceptance. The
  package will then undergo a formal review and recommendations for
  acceptance regarding other Bioconductor standards will be addressed.
  [ ]
  [X ] My package addresses statistical or bioinformatic issues related
  to the analysis and comprehension of high throughput genomic data.
  [ ]
  [X ] I am committed to the long-term maintenance of my package. This
  includes monitoring the support site for issues that users may
  have, subscribing to the bioc-devel mailing list to stay aware
  of developments in the Bioconductor community, responding promptly
  to requests for updates from the Core team in response to changes in
  R or underlying software.
  [ ]
  [X ] I understand it is my responsibility to maintain a valid, active
  email in the DESCRIPTION of my package. This email should allow emails
  from noreply@bioconductor.org and BBS-noreply@bioconductor.org to allow
  automatic notifications from the Bioconductor team concerning my package.
  [ ]
  [ X] I am familiar with the Bioconductor code of conduct and
  agree to abide by it.

I am familiar with the essential aspects of Bioconductor software management, including:

• [X ] The 'devel' branch for new packages and features.[ ] The stable 'release' branch, made available every six
  months, for bug fixes.[ ] Bioconductor version control using Git
  (optionally via GitHub).

For questions/help about the submission process, including questions about the output of the automatic reports generated by the SPB (Single Package Builder), please use the #package-submission channel of our Community Slack. Follow the link on the home page of the Bioconductor website to sign up.

[lshep]

A SummarizedExperiment object is the main class object for Bioconductor omics related work. It would be a requirement to integrate this class structure into the pipeline that is created. While some see it as added complexity having a defined class structure introduces validity and systematic functionality to avoid accidentally errors when working with data.frames. You are welcome to keep the ability to use a simple data.frame structure, but it should also be able to include the common Bioconductor class as input directly.

Also when I try to R CMD build your package it is currently crashing my computer. Have you tried this locally?

[xuz1]

Thanks for the suggestions, the package can take SummarizedExperiment object as input directly now. I also simplified the vignettes to shorten the running time. I tried R CMD build, no error or warning. I also checked the package using "R CMD check" and "R CMD BioCheck" and did not see any error or warnings. Could you check whether all the problems have been fixed. Thanks.

[bioc-issue-bot]

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