Dear Dr. Beyer,

Thank you very much for submitting your manuscript "Optimizing Network Propagation for Multi-Omics Data Integration" for consideration at PLOS Computational Biology.

As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

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[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

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Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Teresa M. Przytycka

Associate Editor

PLOS Computational Biology

Jian Ma

Deputy Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: The paper is a pleasure to read. It studies the algorithm of network propagation and suggests methods to normalize its scores and optimize its parameters. It is well structured and provides all necessary algorithmic details.

However, it seems to neglect previous literature on this subject which is critical to its assessment.

Specifically, there have been several previous attempts for normalization of propagation scores to derive non-biased or even statistical scores. It is essential to compare the paper's results to those methods to determine the performance of the currently suggested method (row normalization). For a recent reference that reviews also earlier work in this regard see

"NetCore: a network propagation approach using node coreness", NAR 2020.

Additional comments:

- topology bias in my mind should be defined with respect to an empty prior which also coincides with setting alpha=1.

- In figure 1, can the authors compute the significance of the difference between hubs and non-hubs?

- it is mentioned that previous studies have chosen alpha arbitrarily but I don't think this is the case - rather performance for different alpha values was assessed.

- the limitations of the parameter selection method - e.g. having data with several replicates - should be clearly stated

Reviewer #2: Network diffusion approaches are commonly used for a variety of applications in biology – functional annotation, imputation, smoothing, etc. There are multiple parameterized approaches out there with little systematic comparison of various approaches and parameters. The authors take on this need and provide a systematic comparative analyses of network diffusion methods and parameters.

The work is well motivated, well executed and very well presented. The approaches seem sound. Of the various (matrix normalization) approaches, the results recommend against some of them based on topology bias (the result is biased by the network topology) one is still left with very little idea of which parameters to use for a specific application. Although this work is an honest exploration of a genuine challenge, I have several comments (hopefully to strengthen the work):

Major

1. Page 11: There are 6 young and 24 old mice. Why only 3 were used for inter-sample variation analysis?

2. Page 11: Authors have presented MSE across genes with various spreading parameter. It would be more informative to look at the distribution of errors across genes, and not just an overall MSE. And if there is a large variability in error for genes, what characterizes the genes with higher error v low error (something to do with network topology perhaps)?

3. Page 12: For the inter-replicate consistency, the improvement is minimal at best and in some cases achieved at alpha=1, which is not very useful. What is a biologist supposed to make of this? Some considered discussion is needed at the minimum.

4. Fig 5A-D: This is nice, but please quantify what fraction of such genes/proteins with missing value were deemed significantly differential based on imputed value.

5. Fig 5E: Is it correct that ONLY 4 genes are differential after multiple testing correction? Any comments on this? If so, is this even a good data to be analyzing?

6. Overall, the results are quite mixed in terms of showing the value of network propagation. Are there range of parameter values that can be recommended that covers the optimal choice across various applications and various optimality criteria? Without this, what is the impact of this work for actual application of these methods?

7. Even though, based on topological bias, which manifests itself only at the extreme levels of alpha value, authors have ruled out certain smoothing approaches (which by the way has been used a lot), it will still be useful to include those methods in the evaluations – it may still be useful for certain applications.

8. Finally, given that there is SO MUCH data out there, the application seems very narrow. I would like to see somewhat broader benchmarking, especially using datasets where the key genes are established.

Minor

9. Supplementary Fig 3: Y-axis should be labelled ‘inter-sample..’, not ‘within-sample..’

10. Page 12: wherever correlations are used, please use Spearman correlation to avoid outlier effect, and specify.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code 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 and code 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 or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: None

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

Figure Files:

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Dear Dr. Beyer,

We are pleased to inform you that your manuscript 'Optimizing Network Propagation for Multi-Omics Data Integration' has been provisionally accepted for publication in PLOS Computational Biology.

Before your manuscript can be formally accepted you will need to complete some formatting changes, which you will receive in a follow up email. A member of our team will be in touch with a set of requests.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Teresa M. Przytycka

Associate Editor

PLOS Computational Biology

Jian Ma

Deputy Editor

PLOS Computational Biology

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: I am satisfied with the revision and recommend publication.

Reviewer #2: I am satisfied with the authors' response to my initial comments.

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Have the authors made all data and (if applicable) computational code underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data and code 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 and code 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 or code —e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: None

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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.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No