Dear Ms. Huang,

Thank you very much for submitting your manuscript "Supervised learning and model analysis with compositional data" 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.

The reviewers both agree on the value of the study, an opinion we share, but request additional clarifications. We note that a common theme is both reviewers wish to see a clearer statement of how the proposed methods compared to others, which we agree is important. As an additional editorial request, we ask that the authors rework the figures to avoid the use of very small fonts (particularly Fig 1 [bottom right] and Fig 4).

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,

Luis Pedro Coelho

Academic Editor

PLOS Computational Biology

Kiran Patil

Section Editor

PLOS Computational Biology

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

The reviewers both agree on the value of the study, an opinion we share, but request additional clarifications. We note that a common theme is both reviewers wish to see a clearer statement of how the proposed methods compared to others, which we agree is important. As an additional editorial request, we ask that the authors rework the figures to avoid the use of very small fonts (particularly Fig 1 [bottom right] and Fig 4).

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: The manuscript by Shimeng Huang et al. proposes KernelBiome, a kernel-based nonparametric regression and classification framework for compositional data. The method is specifically developed to deal with sparse compositional data and can incorporate prior knowledge in terms of phylogenetic structure. The algorithm is validated experimentally on 33 publicly available microbiome datasets and compared with state-of-the-art solutions. The code is available as an open-source python package.

The topic involved in the paper is suitable for publication in PLOS Computational Biology.

I find the methodological solution quite interesting. It is described in detail in both the main paper and the Supplementary Material. I have more comments about the experimental validation of the proposed solution:

1. As a general comment, the manuscript has a quite extensive supplementary material in terms of Appendix, while the main text is more limited, especially in terms of main Figures. I feel that some of the more important results/figures may be moved from the supplementary to the main text.

2. Following the previous point, the comparison among different classifiers is summarized in Figure 3 for only a fraction of the considered datasets (8 among 33 if I understand correctly). I think it would be important to have a figure here that summarizes the results for all considered datasets.

3. Despite the extensive validation, I still don't get to which extent the proposed solution outperforms the existing ones. For example, in how many cases/datasets the proposed method outperforms the other ones? Can you suggest (or not) your solution based on some data characteristics?

4. Could you add a statistical test to evaluate if differences in terms of accuracies are statistically significant?

5. I find interesting that the method can deal with prior information. Which is the added value of this information in terms of classification accuracies? I think it would be relevant performing a comparison in this direction (i.e., comparing results by incorporating or not the prior information).

6. It is not very clear to me which are the free parameters that should be set for the proposed solution. In case, a sensitivity analysis to them should be performed.

7. What in terms of computational complexity of the proposed solution with respect to the compared ones? Please add some empirical evaluations.

Reviewer #2: In this manuscript, Huang et al., propose a kernel-based nonparametric regression and classification framework to address the challenges of compositionality and sparsity in analyzing compositional data. The authors compared the proposed framework with existing methods on publicly available microbiome datasets. Overall, I found the paper to be well-written and well-organized. I have some comments for the authors.

1. Accuracy and MSE are applied in the classification and regression tasks, respectively. In classification tasks with unbalanced data, I don't think accuracy and AUROC are appropriate metrics. Therefore, I am highly concerned with the performance presented in the manuscript. Moreover, MSE and RMSE are both commonly used metrics for evaluating the performance of regression models. MSE is in square units of the target variable, which can make it difficult to interpret the results.

2. Line 357; Please consider add more details on the baseline.

3. Line 373-375: The authors claimed that "On all datasets KernelBiome achieves the best or close to best performance, indicating that the proposed procedure is well-adapted to microbiome data." However, this is not true at all when you look at Figure 6. In some case, the performance of KernelBiome is almost the worst.

**********

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

**********

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

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. Huang,

Thank you very much for submitting your manuscript "Supervised learning and model analysis with compositional data" 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. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

All scientific questions have been address. We agree with reviewer #2 that adding the p-value to the figures is best practices.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

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

[1] A letter containing a detailed list of your responses to all 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.

Thank you again for your submission to our journal. 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,

Luis Pedro Coelho

Academic Editor

PLOS Computational Biology

Kiran Patil

Section Editor

PLOS Computational Biology

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

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

All scientific questions have been address. We agree with reviewer #2 that adding the p-value to the figures is best practices.

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: I feel the authors have answered positively to my previous comments and modified the manuscript according to them.

Reviewer #2: All my comments have been adequately addressed. On another note, the statistical P value should present with all the figures when comparing different methods. By including P-values, readers can better assess the significance of the findings and make informed interpretations.

**********

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

**********

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

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

References:

Review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript.

If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Dear Dr. Huang,

We are pleased to inform you that your manuscript 'Supervised learning and model analysis with compositional data' 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,

Luis Pedro Coelho

Academic Editor

PLOS Computational Biology

Kiran Patil

Section Editor

PLOS Computational Biology

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