PONE-D-24-20999GAN-WGCNA: calculating gene modules as a way to find key intermediate regulators in cocaine addictionPLOS 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: Yes

Reviewer #2: Yes

Reviewer #3: Partly

Reviewer #4: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: I Don't Know

Reviewer #4: 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

Reviewer #3: Yes

Reviewer #4: Yes

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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: Yes

Reviewer #3: Yes

Reviewer #4: 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 manuscript presents a novel analytical pipeline combining Generative Adversarial Networks (GAN) with Weighted Gene Co-expression Network Analysis (WGCNA) to identify key regulatory genes involved in cocaine addiction. Utilizing time-series gene expression data from C57BL/6J mice subjected to two weeks of cocaine self-administration, the study leverages GAN to generate intermediate transcriptome profiles, enabling detailed analysis of gene module interactions over time. Key findings include the identification of two significant genes, Alcam and Celf4, which show high correlation with addictive behavior. The study demonstrates the utility of GAN-WGCNA in uncovering hidden intermediate regulators, providing valuable insights into the spatiotemporal dynamics of gene expression in cocaine addiction.

Here are five technical or conceptual questions based on the provided manuscript titled "GAN-WGCNA: calculating gene modules as a way to find key intermediate regulators in cocaine addiction":

1. How does the integration of Generative Adversarial Networks (GAN) with Weighted Gene Co-expression Network Analysis (WGCNA) enhance the identification of key gene modules involved in cocaine addiction compared to traditional methods?

2. What are the specific biological implications of the identified intermediate genes (Alcam and Celf4) in the context of cocaine addiction, and how might these genes influence addiction-related behaviors and neural processes?

3. Can you elaborate on the methodology used to ensure the statistical significance and biological relevance of the generated intermediate gene expression profiles using the GAN approach?

4. What challenges did the researchers encounter when aligning temporal gene expression profiles with cocaine addiction stages, and how were these challenges addressed in the study?

5. How does the rDEG (rescued differentially expressed gene) method contribute to the identification of intermediate regulatory genes, and what are the advantages of this approach over traditional DEG analysis in the context of cocaine addiction research?

Here are the grammatical and phrasing errors identified in the provided manuscript along with corrections:

Title and Abstract

1. Title:

- Error: "GAN-WGCNA: calculating gene modules as a way to find key intermediate regulators in cocaine addiction"

- Correction: "GAN-WGCNA: Calculating Gene Modules to Identify Key Intermediate Regulators in Cocaine Addiction"

2. Abstract, Line 3:

- Error: "Spatio-temporally enriched NGS data contain important underlying regulatory machinery of biological processes."

- Correction: "Spatio-temporally enriched NGS data contain important underlying regulatory mechanisms of biological processes."

3. Abstract, Line 4-5:

- Error: "Generative adversarial network (GAN) has been used to augment biological data so that it can describe hidden intermediate time-series gene expression profiles during specific biological processes."

- Correction: "Generative adversarial networks (GANs) have been used to augment biological data to describe hidden intermediate time-series gene expression profiles during specific biological processes."

4. Abstract, Line 6-7:

- Error: "The development of a pipeline that uses an augmented time-series gene expression profile is needed to provide an unbiased systemic level map of biological processes and tests for statistical significance of the generated dataset, which leads to discovery of hidden intermediate regulator."

- Correction: "Developing a pipeline that uses augmented time-series gene expression profiles is needed to provide an unbiased systemic-level map of biological processes and test for the statistical significance of the generated dataset, leading to the discovery of hidden intermediate regulators."

Introduction

1. Introduction, Line 1-2:

- Error: "Complex time-series interplay of genes can be observed in various biological processes, such as the cell cycle(1-3), circadian rhythm(4, 5), development(6), and pathogenesis."

- Correction: "Complex time-series gene interactions can be observed in various biological processes, such as the cell cycle (1-3), circadian rhythm (4, 5), development (6), and pathogenesis."

2. Introduction, Line 6-7:

- Error: "Functionality of key factors at specific spatiotemporal points – where, when, and how–is important to this process."

- Correction: "The functionality of key factors at specific spatiotemporal points—where, when, and how—is important to this process."

3. Introduction, Line 9:

- Error: "Typical single-cell RNA sequencing data have 20–30,000 cells with over 10,000 genes."

- Correction: "Typical single-cell RNA sequencing data consist of 20,000–30,000 cells with over 10,000 genes."

4. Introduction, Line 13:

- Error: "Although the network is spatiotemporally complex, in most cases, the current pipeline provides only weak or no spatiotemporal regulatory information."

- Correction: "Although the network is spatiotemporally complex, in most cases, the current pipeline provides only weak or no spatiotemporal regulatory information."

Methods

1. Methods, Line 2:

- Error: "Detailed method is described in Supplementary note 1."

- Correction: "The detailed method is described in Supplementary Note 1."

2. Methods, Line 4:

- Error: "Detailed method is described in Supplementary note 2."

- Correction: "The detailed method is described in Supplementary Note 2."

3. Methods, Line 6:

- Error: "For example, we used the RMSprop optimizer instead of the AdamOptimizer."

- Correction: "For example, we used the RMSprop optimizer instead of the Adam Optimizer."

4. Methods, Line 8:

- Error: "This process was repeated until a successful resembled fake generation was obtained using different NumPy seed values."

- Correction: "This process was repeated until a successful resembled fake generation was obtained using different NumPy seed values."

Results

1. Results, Line 1:

- Error: "In a previous study (18), transcriptome-wide regulation was evaluated in six reward-associated brain regions (PFC, DStr;CPU, NAc, BLA, vHIP, VTA) by comparing DEG analysis in different contexts (acute, re-exposure, etc.) and combining the behavioral addiction index."

- Correction: "In a previous study (18), transcriptome-wide regulation was evaluated in six reward-associated brain regions (PFC, DStr, CPU, NAc, BLA, vHIP, VTA) by comparing DEG analysis in different contexts (acute, re-exposure, etc.) and combining the behavioral addiction index."

2. Results, Line 3:

- Error: "The GAN method uses bulk mRNA-seq data as the training input and generates a time-series intermediate transcriptome using latent space interpolation."

- Correction: "The GAN method uses bulk mRNA-seq data as the training input and generates a time-series intermediate transcriptome using latent space interpolation."

3. Results, Line 5:

- Error: "The generated gene expression profile provided enriched spatiotemporal information."

- Correction: "The generated gene expression profile provided enriched spatiotemporal information."

4. Results, Line 7:

- Error: "The expression profile of the Creb family shows diverse properties of spatiotemporal gene expression changes, revealing subfamily specific profile patterns."

- Correction: "The expression profile of the Creb family shows diverse properties of spatiotemporal gene expression changes, revealing subfamily-specific profile patterns."

Discussion

1. Discussion, Line 1:

- Error: "Machine learning is a promising computational method that can be used to overcome existing limitations in many domains, including biological research (42)."

- Correction: "Machine learning is a promising computational method that can be used to overcome existing limitations in many domains, including biological research (42)."

2. Discussion, Line 4:

- Error: "We proposed a GAN-WGCNA method using generated gene expression data, which is an unbiased systemic approach for the full utilization of high temporal resolution."

- Correction: "We proposed a GAN-WGCNA method using generated gene expression data, which is an unbiased systemic approach for the full utilization of high temporal resolution."

3. Discussion, Line 6:

- Error: "These approaches showed that gene modules in the NAc are mostly involved in addiction processes which account for previous studies (16, 26, 44, 45)."

- Correction: "These approaches showed that gene modules in the NAc are mostly involved in addiction processes, consistent with previous studies (16, 26, 44, 45)."

4. Discussion, Line 10:

- Error: "Most genes belong to the very first or last time points because their expression profile simply decreases or increases, making it meaningless to some extent."

- Correction: "Most genes belong to the very first or last time points because their expression profiles simply decrease or increase, making it meaningless to some extent."

5. Discussion, Line 12:

- Error: "An alternative algorithm or principle is required for generated data that have a higher resolution or dimensionality compared to traditional experimental data."

- Correction: "An alternative algorithm or principle is required for generated data that have a higher resolution or dimensionality compared to traditional experimental data."

Reviewer #2: The authors presents a study for enhancing the resolution and analysis of time-series gene expression data, leading to the identification of critical genes involved in cocaine addiction. The combination of GAN and WGCNA represents a significant advancement in the field of bioinformatics and addiction research. I would like to recommend this manuscript for publication after the authors address/answer the following comments.

In the introduction section, the authors briefly described the two methods employed in this study. For the GAN-WGCNA, which is the combination of the GAN and WGCNA methods, it would be beneficial for the reader to understand the importance by adding more details, such as how these two models are correlated.

In the “Wasserstein Generative Adversarial Networks with Gradient Penalty Loss” section, the authors explained the modifications made to the previous WGAN-GP model and achieved a Pearson correlation value of 0.95. Have you tried further modifying the model to increase the correlation value closer to 1?

Could you update some figures, such as Figure 2, 4b, to a higher resolution in order to clearly present the data points?

In the conclusion section, the authors present the Alcam and Celf4 as the miss cocaine addiction gene in the previous study. It would be advantageous to revisit the biological significance of these genes to underscore their relevance in the context of this study.

Reviewer #3: The paper titled "GAN-WGCNA: calculating gene modules as a way to find key intermediate regulators in cocaine addiction" introduces an approach to decipher the time-series interactions of genes, which is critical for understanding the diagnosis and treatment of diseases. Specifically, this study focuses on cocaine addiction. The authors developed a pipeline that leverages Generative Adversarial Networks (GAN) to enhance biological data, paired with Weighted Gene Co-expression Network Analysis (WGCNA), to identify hidden intermediate regulators during biological processes.

In their analysis, the authors examined a transcriptome dataset from a two-week cocaine self-administration experiment in C57BL/6J mice. Through the GAN-WGCNA method, they identified two genes, Alcam and Celf4, as significant intermediaries correlated with addiction behavior. The study emphasizes that these genes showed notable statistical significance in their correlation with addiction behavior, and their expression and co-regulation were mapped across time, brain regions, and biological processes.

I think this idea is interesting. However, there are several concerns that lead me to recommend rejecting this paper. These concerns include technical novelty, lack of good comparison and validation experiments, insufficient description of methods, writing issues, and potential concerns about their idea.

1. Method Paper Clarity

If this is intended to be a method paper, the abstract and introduction should be rewritten to better reflect that. The abstract should start by mentioning the key gap in current techniques. Additionally, validating your method in at least two datasets would strengthen your claims, as currently, only one is used. It is also important to highlight the novelty of this approach more clearly.

2. Literature Review

In the introduction, the statement that LASSO isn’t optimal for feature reduction is understandable. However, LASSO is a fundamental method, and its concept is different from GAN-WGCNA. It would be beneficial to cite more papers with similar ideas to handle such situations. How do other researchers manage the lack of data samples when doing similar work? More comprehensive literature review for this specific part would be helpful. While the next paragraph cites several papers using GAN for augmenting biological data, it is crucial to explain why GAN is particularly suitable for this context.

3. Methods

The method section would benefit from more details on how GAN was used to augment the data.

(1) Providing necessary mathematical formulations and explaining how the model was trained would be valuable.

(2) It is also important to address how you ensured that the model is not overfitting, especially if a training-test split was not used.

(3) Some evidence supporting the choice of GAN for modeling gene expression would also strengthen the manuscript. Given the fact GAN is notorious difficult to train all the times.

4. Conceptually I disagree with this paper

The paper claims that GAN is powerful for augmenting data points, which is true. However, GAN is typically used to generate data points that are as identical to the original as possible. It would be helpful to clarify how this method can be used to understand the time-series interplay of genes for diagnosis and treatment of disease. Explaining how the generated data points contribute to the analysis would address potential concerns. For example, won’t a better method to be variational autoencoder? By playing with the key element in the hidden states you can actually have some artificial datapoints in the middle of two categories, which might mimic the time interplay better.

5. Results Section:

The statement "GAN enables a calculation of correlation between gene module and behavioral data” needs more clarity. It would be useful to explain why the original dataset does not support similar analysis and whether other data augmentation methods could achieve similar results. Highlighting how your method differs from existing approaches, or the original data would be beneficial. The manuscript should clearly show the additional information or insights provided by your method.

6. Suggested Experiment: A useful experiment could be using a large dataset and applying your GAN-WGCNA method on a subset of that data. Checking if you can find differentially expressed genes (DEG) or GO pathways identifiable only in the larger cohort but not in the smaller subset could validate the method’s effectiveness.

7. Result Section Organization: Some result sections are quite long and may be confusing for readers. Consider restructuring them to make the manuscript more organized and easier to follow.

Given my concerns outlined above, I am worried that the authors won't be able to address all these issues in a short amount of time. Therefore, I might recommend rejecting this paper and suggesting that the authors fix these concerns and resubmit it later.

Reviewer #4: With this manuscript titled “GAN-WGCNA: calculating gene modules as a way to find key intermediate regulators in cocaine addiction” the authors analyzed (1) a transcriptome dataset of two weeks of cocaine self-administration in C57BL/6J mice. (2) found two genes (Alcam and Celf4) as intermediate significant genes that showed high correlation with cocaine addiction behavior via GAN-WGCNA analysis method.

The manuscript is pleasant to read. But I still have some concerns that need to be addressed.

Comments:

1. Since the author mentioned the raw data is from previous literature, I read the previous paper and found they have data from six groups mice for cocaine self-administration. The current manuscript results based on all 6 groups or limited groups?

2. The original paper (Ref.18) found cocaine addiction genes highly related with basolateral amygdala (BLA), but the current manuscript showed different results (Compare BLA vs. NAC). How to explain it?

3. Cocaine withdraw data is also important, it will be good to check if Alcam and Celf4 genes are also related to cocaine short time (24 hr) or long time (30 d) withdraw with GAN-WGCNA since the ref.18 had found some interesting results from the short and long withdraw groups.

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Reviewer #1: Yes: peng wang

Reviewer #2: Yes: Shuyuan Zhang

Reviewer #3: No

Reviewer #4: No

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PONE-D-24-20999R1GAN-WGCNA: Calculating Gene Modules to Identify Key Intermediate Regulators in Cocaine AddictionPLOS ONE

Dear Dr. Yu,

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.

Please submit your revised manuscript by Oct 11 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,

Guanghui Liu

Academic Editor

PLOS ONE

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Please 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.

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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 #1: All comments have been addressed

Reviewer #2: (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 #1: Yes

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

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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

Reviewer #2: 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

Reviewer #2: Yes

**********

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: The authors have thoroughly addressed all of my concerns. I have no further questions and recommend proceeding with the acceptance process according to the journal's guidelines.

Reviewer #2: The authors present a study for enhancing the resolution and analysis of time-series gene expression data, leading to the identification of critical genes involved in cocaine addiction. After the revision, I would like to recommend this manuscript after the authors address/answer the following comment.

For the comment 1, the authors added the extra paragraph to explain the correlation between GAN and WGCNA methods. Although the extra detailed paragraph really helped to understand the relationship between two models, it will be beneficial to including the reference for several statement, such as “WGCNA is one of well-accepted conventional methods that….”

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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.

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: peng wang

Reviewer #2: No

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GAN-WGCNA: Calculating Gene Modules to Identify Key Intermediate Regulators in Cocaine Addiction

PONE-D-24-20999R2

Dear Dr. Yu,

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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If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Guanghui Liu

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

Reviewer #2: 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

Reviewer #2: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

**********

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: (No Response)

Reviewer #2: 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

Reviewer #2: 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: The authors have thoroughly addressed all of my concerns. I have no further questions and recommend proceeding with the acceptance process according to the journal's guidelines.

Reviewer #2: The authors present a study for enhancing the resolution and analysis of time-series gene expression data, leading to the identification of critical genes involved in cocaine addiction. After the revision, I would like to recommend this manuscript.

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Reviewer #1: Yes: peng wang

Reviewer #2: No

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