Dear Dr. Hu,

Thank you very much for submitting your manuscript "Testing microbiome associations with survival times at both the community and individual taxon levels" 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.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

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Sincerely,

Dina Schneidman-Duhovny

Software 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: In this manuscript, Hu et al. extended their LDM framework to analyze survival outcomes. By treating the Martingale or deviance residuals as a covariate, their LDM framework can be conveniently applied. One nice thing about the proposed method is that it achieves both the community-level and individual taxa-level tests. Simulations and real data analysis demonstrated the superior performance of the proposed method to the ad hoc Cox model (individual taxa tests) and MiKRAT-S and OMiSA (global test). As clinical research has been increasingly adding the microbiome component, statistical methods for integrating the microbiome data into survival analysis are urgently needed. However, previous research mainly focused on community-level tests. Methods for individual taxa-level tests predominantly use the traditional Cox regression model by treating the taxon abundance as a covariate. It is not clear whether this method is valid or efficient. In this work, the authors revealed that the Cox model could not control the type I error properly, which is quite surprising. Overall, I believe the work is a nice contribution to the microbiome community. Once fully validated, I envision it to be an essential tool in statistical analyses of microbiome data. I have a couple of minor questions and comments that need the authors’ feedback.

1. I do not quite understand why the traditional Cox model has such a high FDR inflation. If the null model is generated according to the Cox model, I will expect the Cox model could control the type I error very well.

2. The authors did not mention/address the potential compositional effects. For example, what if several highly abundant taxa are associated with survival in the same direction? I think the authors need to address it in the discussion by providing some possible solutions, e.g., using robust normalization (GMPR, CSS, etc.).

3. In the simulation, the 20% FDR cutoff may be too high. I think the authors should increase the signal strength instead of using a higher FDR since the real data uses 10% FDR.

4. In the discussion, the authors mentioned the LDM-omni3. Could the authors provide the omni3 p-value for the real data?

Reviewer #2: In this manuscript, Hu and colleagues present a method for associating

microbiome with censored survival times. They extend their previous method (and

package, so that the method is available for others) to be able to both detect

community associations as well as associating particular taxa with the outcomes.

Overall, the methods appear to be well done and the benchmarks based on

simulated data do show the advantages of this method.

However, the application to real data shown in the current version is still

limited. I think it would strengthen the manuscript to present more of the

analyses of the aGVHD data in the main text. In particular, I recommend moving

Fig S7 to a main figure. One important claim that the authors make is that

applying the Cox model in an ad hoc model leads to unacceptably high false

discovery rates. This is demonstrated in simulated data, but it would be

important to see the effect in real data as well.

Although not something that I would personally consider to be strictly

required, adding some shotgun metagenomics data analyses would also further

bolster the case that this method is widely applicable. In principle, it should

be possible to apply the method; but shotgun data does have different error

profiles.

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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: Yes

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Reviewer #1: No

Reviewer #2: No

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While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

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Reproducibility:

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Dear Dr. Hu,

Thank you very much for submitting your manuscript "Testing microbiome associations with survival times at both the community and individual taxon levels" 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.

Please address the point raised by Reviewer 2.

We cannot make any decision about publication until we have seen the revised manuscript and your response to the reviewers' comments. Your revised manuscript is also likely to be sent to reviewers for further evaluation.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

[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).

Important additional instructions are given below your reviewer comments.

Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

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,

Dina Schneidman-Duhovny

Software Editor

PLOS Computational Biology

Dina Schneidman-Duhovny

Software Editor

PLOS Computational Biology

***********************

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: The authors have done a great job in addressing my comments. I think the manuscript is in very good shape and ready for publication. Thanks for this important work!

Reviewer #2: I thank the authors for their answers and improvements to the manuscript.

However, unless I am mistaken, the new results in Table 2 (related to the results of the Cox model) actually show the opposite of what was claimed based on simulated data: the Cox model returns fewer positives (in several instances, zero taxa) than the alternatives. Thus, it becomes hard to see how this corresponds to unacceptably high false positive rates (rather than its opposite, a lack of statistical power). I feel that this point should be clarified before publication.

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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

**********

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: Yes: Jun Chen

Reviewer #2: No

Figure Files:

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Dear Dr. Hu,

We are pleased to inform you that your manuscript 'Testing microbiome associations with survival times at both the community and individual taxon levels' has been provisionally accepted for publication in PLOS Computational Biology.

Please update the final version based on the comments of the Reviewer #2.

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,

Dina Schneidman

Software 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 #2: My concerns have been addressed as the new paragraph explicitly addresses the discrepancy between simulation and real data.

I will defer to the editor as to whether it is also appropriate to make it more explicit earlier that references [10,11] which are used to support the statement that "it is known that small sample sizes and sparse count data may lead to inflated type I error when using the Cox model" are not microbiome-related (which may explain why the results in this manuscript contradict this statement).

The second sentence in the new paragraph should probably read "Conversely, in our analysis of the aGVHD data, we found that the Cox model made fewer discoveries and particularly zero discoveries based on the relative abundance data." ("discoveries" and not "discovery").

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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 #2: Yes

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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.

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Reviewer #2: No