[REVIEW]: Easy16S: a user-friendly Shiny web-service for exploration and visualization of microbiome data.
editorialbot opened this issue · comments
Submitting author: @CedricMidoux (Cédric Midoux)
Repository: https://forgemia.inra.fr/migale/easy16s
Branch with paper.md (empty if default branch):
Version: v24.03
Editor: @fboehm
Reviewers: @adrientaudiere, @tmaklin
Archive: Pending
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Markdown: [](https://joss.theoj.org/papers/e03bb9530fd2e1c0621e35352b71691e)
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Checklists
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Software report:
github.com/AlDanial/cloc v 1.90 T=0.03 s (2046.4 files/s, 269308.5 lines/s)
-------------------------------------------------------------------------------
Language files blank comment code
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R 43 690 1342 3952
Markdown 5 190 0 781
TeX 1 17 0 193
YAML 4 14 7 142
JSON 1 0 0 19
Rmd 2 104 152 12
Dockerfile 1 0 1 10
CSS 1 1 0 6
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SUM: 58 1016 1502 5115
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Commit count by author:
109 Cedric Midoux
13 Mahendra Mariadassou
Reference check summary (note 'MISSING' DOIs are suggestions that need verification):
OK DOIs
- 10.1186/s40168-021-01013-0 is OK
- 10.1038/ismej.2014.202 is OK
- 10.1038/nmeth.3869 is OK
- 10.1186/s13059-014-0550-8 is OK
- 10.1093/bioinformatics/btx791 is OK
- 10.1111/1755-0998.13847 is OK
- 10.1111/geb.13118 is OK
- 10.1038/nature12506 is OK
- 10.1371/journal.pcbi.1005404 is OK
- 10.18129/b9.bioc.microbiome is OK
- 10.15454/1.5572390655343293E12 is OK
- 10.1371/journal.pone.0061217 is OK
- 10.1016/j.watres.2016.05.041 is OK
- 10.1038/nmeth.f.303 is OK
- 10.1186/s12859-020-03666-4 is OK
- 10.1093/bioinformatics/btu616 is OK
MISSING DOIs
- No DOI given, and none found for title: esquisse: Explore and Visualize Your Data Interact...
- No DOI given, and none found for title: shiny: Web Application Framework for R
INVALID DOIs
- None
Paper file info:
📄 Wordcount for paper.md is 849
✅ The paper includes a Statement of need section
License info:
🟡 License found: GNU Affero General Public License v3.0 (Check here for OSI approval)
Review checklist for @adrientaudiere
Conflict of interest
- I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.
Code of Conduct
- I confirm that I read and will adhere to the JOSS code of conduct.
General checks
- Repository: Is the source code for this software available at the https://forgemia.inra.fr/migale/easy16s?
- License: Does the repository contain a plain-text LICENSE or COPYING file with the contents of an OSI approved software license?
- Contribution and authorship: Has the submitting author (@CedricMidoux) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
- Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
- Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
- Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
- Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.
Functionality
- Installation: Does installation proceed as outlined in the documentation?
- Functionality: Have the functional claims of the software been confirmed?
- Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)
Documentation
- A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
- Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
- Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
- Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
- Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
- Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support
Software paper
- Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
- A statement of need: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
- State of the field: Do the authors describe how this software compares to other commonly-used packages?
- Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
- References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?
Disclaimer: I am the developer of the MiscMetabar package, so I cite it a lot. My goal is to illustrate my review; I don't want to encourage authors to integrate MiscMetabar into their package.
Easy16S is a shiny-based application that facilitates simple analysis and visualization of metabarcoding data. I would first like to congratulate the authors for their effort to simplify the interface and to produce software that's pleasant to use. The paper is good and provides a good summary of the software's functions. I have two major suggestions to improve Easy16S and multiple little ones. My major suggestions are not true issues, so I wrote all my review in one text below. If you prefer, I can split the comments into separate gitlab issues.
Major comments/suggestions
Explain or at least alert about the complexity of interpreting results from very diverse metabarcoding studies
Focus on simplicity and rapidity is legitimate, but it may lead to biased figures or even false conclusions. As you notice in the text, if a biologist can emancipate from bioinformatician and biostatistician using your tool, the downside is the risk of misinterpretation of results. I think you should at least mention this risk in the main text. I think this can also be a force in your package. You, as bio-informatician/bio-statistician, choose default methods and parameters, and occasionally you even force some parameter values (e.g., the nb of permutation in permanova is not editable, and I think it’s a good decision). You may underline the fact that you put up guardrails in your software to decrease the risk of misusage of statistics.
Some parts are clearly lacking information
-
To transform our data, we have multiple choices that are not well-defined and mixed across methods. The transformation module_1 should be divided by the type of transformations (1. samples filter, 2. taxa filter, 3. abundance-transformation (including rarefaction), 4. agglomeration of taxa, 5. agglomeration of samples (not yet implemented; see for example the function merge_samples2 from speedyseq, also available in the MiscMetabar package), and 6. modification of the taxonomic names). And the targets' audience may need additional explanation for some transformation (e.g.,
spread taxonomyis not clear enough). -
Accumulation curves must show warnings. The only place with a
suppressWarningsin your code is in the accumulation curve panel. I think it is a critical warning. If you want to clarify why we can use accumulation curve without singletons in metabarcording, please put this explanation at the head of the panel. And I am truly interested in reading it because it is not clear to me how accumulation curves are useful.
Minor comments
- Why put 16S in the name, as it is also suitable for ITS or other marker?
- Make even more effort to cite the exact method/software is used. For example, the differential abundance analysis leans on DESeq2 and this is quite hard to find, especially for non-aware biologists (the only info in the information bullet that explains nothing but the word “phyloseq to DESeq2”, quite confusing when you don’t know differential abundance analysis).
- A track of the number of samples/taxa/sequences after each transformation can be very useful. See e.g. function
track_wflow()from the MiscMetabar package. - A venn (
ggvenn_pq()) and/or upset (upset_pq()) plots are very useful in numerous cases with factorial samples variables. - Some functions rely on the
phyloseq.extendpackage which has not integrated changed for 5 years and is only available on github. Is there no way to find alternative or to integrate those functions in your package? - Did you know https://f1000research.com/articles/7-1734/v1 ? May be they deserve a citation?
- The number of sequences may be interesting in the summary panel. Some other information such as the distribution of the number of sequences per sample (at least the minimum and the mean) and the sequences' length (see
summary_plot_pq()from the MiscMetabar package for other ideas) - Can you explain why you set in the README that your package “is tightly integrated with the FROGS sequence processing suite” ?
Specific comments for the case of the dataset data_fungi_mini available in the MiscMetabar package
- alpha-anova does not work with observed or Fisher values (covariate Height ; message : impossible to extend the range of extreme points for f(lower) f(upper) <= 0) but do with shannon. Is it normal?
- There are Na in some sample variables (e.g., Height) and it blocks some analysis. A good point will be a message that makes it clear to remove Na using the transformation panel.
Hello,
Thank you for your review and your suggestions.
I'm going to correct the things you raised (particularly the documentation and user guidance).
I'll get back to you when it's done.
@tmaklin - do you have any questions about getting started with the review? Thanks again!
Review checklist for @tmaklin
Conflict of interest
- I confirm that I have read the JOSS conflict of interest (COI) policy and that: I have no COIs with reviewing this work or that any perceived COIs have been waived by JOSS for the purpose of this review.
Code of Conduct
- I confirm that I read and will adhere to the JOSS code of conduct.
General checks
- Repository: Is the source code for this software available at the https://forgemia.inra.fr/migale/easy16s?
- License: Does the repository contain a plain-text LICENSE or COPYING file with the contents of an OSI approved software license?
- Contribution and authorship: Has the submitting author (@CedricMidoux) made major contributions to the software? Does the full list of paper authors seem appropriate and complete?
- Substantial scholarly effort: Does this submission meet the scope eligibility described in the JOSS guidelines
- Data sharing: If the paper contains original data, data are accessible to the reviewers. If the paper contains no original data, please check this item.
- Reproducibility: If the paper contains original results, results are entirely reproducible by reviewers. If the paper contains no original results, please check this item.
- Human and animal research: If the paper contains original data research on humans subjects or animals, does it comply with JOSS's human participants research policy and/or animal research policy? If the paper contains no such data, please check this item.
Functionality
- Installation: Does installation proceed as outlined in the documentation?
- Functionality: Have the functional claims of the software been confirmed?
- Performance: If there are any performance claims of the software, have they been confirmed? (If there are no claims, please check off this item.)
Documentation
- A statement of need: Do the authors clearly state what problems the software is designed to solve and who the target audience is?
- Installation instructions: Is there a clearly-stated list of dependencies? Ideally these should be handled with an automated package management solution.
- Example usage: Do the authors include examples of how to use the software (ideally to solve real-world analysis problems).
- Functionality documentation: Is the core functionality of the software documented to a satisfactory level (e.g., API method documentation)?
- Automated tests: Are there automated tests or manual steps described so that the functionality of the software can be verified?
- Community guidelines: Are there clear guidelines for third parties wishing to 1) Contribute to the software 2) Report issues or problems with the software 3) Seek support
Software paper
- Summary: Has a clear description of the high-level functionality and purpose of the software for a diverse, non-specialist audience been provided?
- A statement of need: Does the paper have a section titled 'Statement of need' that clearly states what problems the software is designed to solve, who the target audience is, and its relation to other work?
- State of the field: Do the authors describe how this software compares to other commonly-used packages?
- Quality of writing: Is the paper well written (i.e., it does not require editing for structure, language, or writing quality)?
- References: Is the list of references complete, and is everything cited appropriately that should be cited (e.g., papers, datasets, software)? Do references in the text use the proper citation syntax?
@CedricMidoux - Do you have any questions about how to revise the submission materials?
I was waiting for the review from @tmaklin.
I will start correcting the first feedback.
That sounds good, @CedricMidoux ! please let me know if you have questions