PONE-D-23-39381Identification of Hypertension Gene Expression Biomarkers Based on the DeepGCFS AlgorithmPLOS ONE

Dear Dr. Li,

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PLOS ONE

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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: Partly

Reviewer #2: Partly

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

Reviewer #1: Yes

Reviewer #2: No

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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: No

Reviewer #2: No

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4. 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

Reviewer #2: No

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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: In this study, the authors introduce the Deep Graph Clustering Feature Selection (DeepGCFS) algorithm to enhance the identification of hypertension biomarkers. The algorithm employs a graph network to portray gene relationships, integrates link prediction and self-supervised learning, and incorporates a hybrid clustering method with integrated feature selection. However, some drawbacks exist:

1. In the introduction, discuss the advantages and disadvantages of the reviewed literature methods for identifying hypertension biomarkers.

2. The phrase "They also do not take into account the attribute information of features" means that existing methods may overlook the specific characteristics or attributes of features when identifying biomarkers. This needs more clarification on how these attributes are important.

3. Expand on the sentence about feature selection methods, detailing various types such as wrapper and filter selections. Highlight that while wrapper methods capture some feature relationships through model performance, they may not explicitly model dependencies among features, which is crucial in biomarker identification.

4. Strengthen the literature review by including key publications on predicting marker genes associated with hypertension, such as the works by Gao et al. and Jiang et al., emphasizing their methodologies and findings.

1. Gao Y, Qi GX, Jia ZM, Sun YX. Prediction of marker genes associated with hypertension by bioinformatics analyses. Int J Mol Med. 2017 Jul;40(1):137-145. doi: 10.3892/ijmm.2017.3000. Epub 2017 May 25. PMID: 28560446; PMCID: PMC5466388.

2. Jiang C, Jiang W. Lasso algorithm and support vector machine strategy to screen pulmonary arterial hypertension gene diagnostic markers. Scott Med J. 2023 Feb;68(1):21-31. doi: 10.1177/00369330221132158. Epub 2022 Oct 17. PMID: 36253715.

5. In the introduction, elaborate on the proposed approach's foundation in deep graph clustering. Discuss various clustering methods and their pros and cons, including Deepwalk, Node2vec, SDNE, and Weighted Correlation Network Analysis (WGCNA).

6. Explain the criteria used for selecting the highest correlations among all pairs of genes. Specify the threshold considered as "highest" and clarify the process of creating the graph network by merging relevant edges (eGp and eGs).

7. Enhance clarity in the methodology by providing a detailed pseudocode, step by step, color each stage. Encourage sharing the code and dataset on GitHub for transparency.

8. Explain why Mutual Information Feature Selection (MIFS) is used in the final step, providing justification for choosing it over other feature ranking methods.

9. Compare the proposed approach's performance with at least two gene prioritization methods, considering AUC and Gene Ontology (GO) analysis.

10. In GO and KEGG pathway analyses, please perform separate assessments for up-regulated and down-regulated genes.

11. Utilize commonly used cross-validation (5 or 10 folds) instead of 3. Draw a heatmap of identified biomarkers and provide details on the number of genes and edges in the first step.

12. Compare the performance of the proposed approach with WGCNA.

13. For visualizing gene modules, please utilize the widely-used STRING platform, incorporating color-coding to distinguish between up-regulated and down-regulated genes.

Reviewer #2: Dataset:

The dataset used for this problem - a graph-theoretic machine learning analysis to identify biomarkers of hypertension - seems to be rather small - just a handful of HT and normal samples. A larger dataset may be necessary; please see comment on benchmarking.

Validation:

The authors have used a GEO dataset for validation, but have performed three-fold cross-validation with the biomarkers. This is not valid. Even the biomarkers do not replicate on the validation dataset.

There is no train-test split either, and the authors may consult with a statistician for proper validation of their models.

Benchmarking with published models:

Many models exist for prediction of hypertension. The authors have not compared their results with any of the existing models. Existing methods might perform better, this may be studied.

Reproducibility:

No stepwise results have been shared, and data supporting the conclusions has not been shared. No models have been shared, and work cannot be reproduced at all. Even the authors' biomarker replication efforts do not succeed with the validation dataset.

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Reviewer #1: No

Reviewer #2: Yes: Ashok Palaniappan

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PONE-D-23-39381R1Identification of Hypertension Gene Expression Biomarkers Based on the DeepGCFS AlgorithmPLOS ONE

Dear Dr. Li,

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.

You could find the comments by the reviewers, and the decision to this paper is Major Revise. We advise the authors revised this paper as the reviewers suggested, and give a point-to-point response for the revised version. We are glad to receive your new version of this work.

Please submit your revised manuscript by Oct 24 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

• A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.
• A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.
• An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Hui Li

Academic Editor

PLOS ONE

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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 #2: (No Response)

Reviewer #3: 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 #2: Partly

Reviewer #3: Yes

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

Reviewer #2: No

Reviewer #3: (No Response)

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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 #2: No

Reviewer #3: No

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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 #2: No

Reviewer #3: 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 #2: With respect to validation, the authors mention that "Response #2：Thank you for the reviewers' suggestions. Based on the sample size and recommendations from statisticians, we adopted the Leave-one-out cross-validation ".

LOOCV remains a form of cross-validation, which is unsuited for external validation. The model already built need not be cross-validated. How does that make sense?

With respect to benchmarking, the authors mention that "Response #3 ： Thank you for the reviewers' suggestions. We have conducted a performance comparison analysis of our proposed method with other methods in the literature. The experimental results are presented in the table."

The authors may be referring to Table 5. From Table 5, it is clear that many previous methods had been developed for this problem. Benchmarking all these models requires a common dataset for reference and evaluation. It is is not clear from the presentation if all the models were evaluated on the same dataset. This may be asserted if true. If not, then it is not meaningful to present this comparison.

Thanks and good wishes.

Reviewer #3: In the revised version, my former comments have been addressed. I have no critical comments except that the wet-lab experiments for validating these findings. I think the authors will conduct them in the near future and they discuss this important issue in this paper.

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Reviewer #2: Yes: Ashok Palaniappan

Reviewer #3: 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. Registration is free. 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 PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

Identification of Hypertension Gene Expression Biomarkers Based on the DeepGCFS Algorithm

PONE-D-23-39381R2

Dear Dr. Li,

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.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Hui Li

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 #4: (No Response)

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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 #4: (No Response)

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

Reviewer #4: (No Response)

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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 #4: (No Response)

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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 #4: (No Response)

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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 #4: The paper is well-written, the novelty is sufficient, and the response to the reviewer was satisfactory.

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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 #4: Yes: Pouya Bolourchi

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