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  • The initial submission of this article was received on August 25th, 2020 and was peer-reviewed by 2 reviewers and the Academic Editor.
  • The Academic Editor made their initial decision on September 18th, 2020.
  • The first revision was submitted on September 30th, 2020 and was reviewed by the Academic Editor.
  • The article was Accepted by the Academic Editor on October 7th, 2020.

Version 0.2 (accepted)

· Oct 7, 2020 · Academic Editor


Thank you for addressing the remaining concerns and congratulations again.

[# PeerJ Staff Note - this decision was reviewed and approved by Keith Crandall, a PeerJ Section Editor covering this Section #]

Version 0.1 (original submission)

· Sep 18, 2020 · Academic Editor

Minor Revisions

The reviewers were very positive with only a few minor questions/issues brought up, primarily by Reviewer 2. Please address these questions as best as possible.


Basic reporting

The article is well written with very few grammatical errors. It provides a good background of the field in the introduction and has sufficient citations to previous literature throughout. Figures are clear and legends provided a good description of the figures.

The introduction provides a nice background on coexpression analyses and methods used to explore datasets to find genes and pathways of interest. It highlights the Mutual Rank method of measuring coexpression and describes the utility of MutRank for coexpression analysis and visualization of results.

Line 76 - double word
Line 78 - Awkward sentence structure

Experimental design

MutRank is coded in R, which makes it relatively accessible to a scientist doing this type of research. It should be simple to integrate into existing analysis pipelines and launch the Shiny visualization tool to further analyze the results. I was able to download MutRank and be up and running with the example dataset in a matter of minutes. The documentation is thorough on what data formatting is necessary and example datasets make it easy to emulate the necessary format for input into the application. The application looks coded in a straightforward way that would make it relatively extensible for future researchers to add other types of scoring algorithms and analyses.

Validity of the findings

No comment

Reviewer 2 ·

Basic reporting

The manuscript was written in unambiguous English and easy to read. Given that this is a manuscript to introduce new analysis tool (and therefore I do not think authors need to review background biological context in detail), I think literatures provided are good enough. Raw dataset (i.e., sample data) to replicate their results is provided. Two examples were illustrated to show their MutRank application is powerful and user-friendly tool to analyze expression data.

Experimental design

The manuscript may meet the scope of PeerJ journal, as the illustrated R-shiny application will contribute to many biologists to analyze their expression data. As authors mentioned in the introduction section, it is important to develop a flexible and user-friendly tool.

The authors clearly explained how to use MutRank application (such as what kind of input data is needed, what kind of parameters to be specified, and what kind of results to be generated), while some of the implemented statistical methods should be clearly explained. For example:

[2.1] In line 149, what is the definition of “top coexpressed genes”? As correlation is assigned for each “pair of two genes”, I could not understand how to define coexpressed genes (e.g., there are top 200 gene pairs showing largest correlation, but what are the top 200 genes?)

[2.2] In line 181, please describe GO term enrichment analysis more clearly. In the enrichment analysis, we have “full set” (such as all genes in the database or all genes in our dataset) and “subset” (such as genes in a module or genes coexpressed with a gene of interest). According to your example 2, I guess you used all genes in the GO database as a full set, but it is not clear.

There are some additional small comments on the methods. See the general comments section.

Validity of the findings

The authors provided two examples, which were used in previous studies, to illustrate the usage and potential of the MutRank application. The results imply that MutRank is a powerful tool to analyze expression data to find a biologically meaningful relationship among genes. Conclusion is clearly stated and supported from the two examples.

Additional comments

For me, who usually analyzes expression data in R, the implemented methods (such as calculation of mutual rank, data visualization via heatmap or network analysis, and the GO enrichment analysis) in this shiny application are not new. However, as there are many biologists or biochemists who is not good at using R, I agree that this application will be helpful for many researchers. Methods and results are clearly explained so that everyone can use this application.

I have some minor comments to be addressed. Read the below:
[4.1] Line 59: Add period after the citation “et al. 2020). Studies in plants”
[4.2] Line 76: Remove one of the duplicated “analyses”
[4.3] Line 76-77: Is the phrase “produce improved results when using raw data compared to PCC” correct? Was the raw data better than PCC?
[4.4] Line 111-113: This is an optional comment. I am not comfortable with the wrap-up sentence “The goal of MutRank is to... biological goals”, because what we can get from MutRank is the coexpressed genes. Probably you implied GO enrichment analysis by the phrase “connecting metabolic phenotypes to genotypes”, but I am not sure if this phrase is appropriate. When reading this phrase at the first time, I thought that we can analyze some phenotypes (e.g., metabolite abundance measured by mass spectrometry) in this application. Find another phrase if possible.
[4.5] Line 178: For the coexpression network visualization, how do you draw edges among genes? Do you simply draw edges between genes with smaller MR value than a threshold? Or do you somehow use PCC?
[4.6] Line 217: It may be better to rephrase “... displayed, as they are absent from the currently selected expression dataset”. I guess you did not include Bx6 and Bx7 in this analysis from the beginning (as they are not in the list of supplementary table 8), because the maize expression dataset (supplementary table 1) does not have these two genes. Then, for example, you should write “Bx6 and Bx7 were excluded from the analysis as they were not included in the expression dataset for this analysis.” (I mean, the word “displayed” may not be appropriate)
[4.7] Line 221: Is the threshold 25 correct? In the supplementary document, it seems you used 100. Please confirm and make correction if needed.
[4.8] Line 249: I think you should not include ZmAN2 in the identified genes, because it is trivial that ZmAN2 is included as a top gene (self-correlation is always one). You should write “identified one additional TPS gene” (and describe it; type I diterpene synthase).
[4.9] Line 256: No need to abbreviate GWAS. This is only used in this sentence, as far as I checked (or you have GWAS in your supplementary files? Then keep this as it is.).
[4.10] Line 199, 200, and 261: Some letters are not displayed in the PDF file.

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