PONE-D-24-22937An application of node and edge nonlinear hypergraph centrality to a protein complex hypernetworkPLOS ONE

Dear Dr. Lawson,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Both reviewers find your work to be very interesting and worthy of publication. However, there are several comments and suggestions that deserve your consideration, particularly those related to improving the clarity of the manuscript and the comparison, if possible, of your method with previous pairwise network approaches.  

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PLOS ONE

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[ This work was supported by The Australian Research Council,through the Centre of Excellence for Plant Success

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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4. Is the manuscript presented in an intelligible fashion and written in standard English?

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The paper deals with using centrality measures to find essential proteins and essential protein complexes when they interact. Traditionally proteins are interpreted as nodes of a network while interactions are understood as edges between two nodes in that network (PIN). The novelty in this paper is that interactions are not determined by just a pair of proteins but by a group of them, and this indeed justifies the use of hypergraphs. With applications in view the authors introduce some sound adaptations on the Tudisco-Higham NSVC model in order to obtain the ranking vector. The paper is clearly written and the model consistently used to achieve their goal on the S. Cervisiae dataset. Therefore I recommend publication.

Still there are some minor comments referring to the generalized power function set introduced by the authors.

1) The role of zero as a value for $a,b$ deserves some additional explanation, especially when a comparison with the three examples given by Tudisco-Higham is made.

2) The authors observe that the rankings obtained don't vary when the variables $a,b,c,d$ belong to the same family, but I miss some addtional justification of why this is so in the examples given.

3) A natural question arises about the vector structure in $\\mathbb{R}^4$ of those families above. Perhaps some comment or hint would be welcome in this direction but I leave it to the authors' choice.

Reviewer #2: This article provides an interesting application of hypergraphs and an eigenvector/eigenvalue based centrality method to identify essential proteins and essential protein complexes. They apply the method to a protein complex hypergraph of S. Cerevisiae. The application of the method and the evaluation were straightforward.

I found the article fairly easy to read. The details in section 2.2 were the most opaque mostly because there is a lot of text and a lot of notation. I'm a mathematician myself (not a biologist!) so this doesn't scare me. But I think it would make it easier to digest if there were a summary table of the different function configurations and power configurations and what they correspond to. You do have Table 1, which is helpful. But I think a more general table earlier on, in addition, would bring even more clarity. As you apply them in section 3 it becomes difficult to flip back and forth to recall intuition.

The reason that I put this as "major revision" rather than "minor" is the following: The authors point out in the first sentence of the discussion that "Research identifying essential proteins through their interactions has been predominantly focused on using graph measures applied to PINs." It would be nice to compare your findings to a comparable graph approach. Does the state of the art graph measure applied to PINs have even higher scores? This is important in convincing biology researchers that it's worth getting into all of this additional structure of hypergraphs and math of the NSVC measures. You could add it as a method to each of your figures as a baseline for comparison (like you have random as a baseline already). I understand the graph approach would only work for essential proteins and not essential complexes, which is already a point in your favor. But does NSVC do better at identifying essential proteins?

A couple of other minor comments:

- On page 2 when you define weighted node degree in terms of w(e) and weighted edge degree in terms of v(i) you haven't defined the w and v functions. I'd suggest to avoid v as a weight function since v_i are vertices.

- When you introduce the NSVC model please provide a bit more intuition at the front. How should we think of the f, g, phi, psi functions? What are x and y really capturing?

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Reviewer #1: No

Reviewer #2: No

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An application of node and edge nonlinear hypergraph centrality to a protein complex hypernetwork

PONE-D-24-22937R1

Dear Dr. Lawson,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Kind regards,

Irene Sendiña-Nadal

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I thank the authors for properly addressing all comments. I am happy to recommend publication now.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #1: No

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