PONE-D-24-23903Comparing statistical learning methods for complex trait prediction from gene expressionPLOS ONE

Dear Dr. Morgante,

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Ashutosh Pandey, Ph.D.

Academic Editor

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: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: 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: Yes

Reviewer #2: Yes

Reviewer #3: 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

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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 study compares statistical learning methods to evaluate the potential of transcriptomic data for accurate prediction of complex traits. For this purpose, the study leverages data from the Drosophila Genetic Reference Panel (DGRP) to evaluate the performance of different prediction models for predicting starvation using transcriptomic data. The study concluded that differences in prediction accuracy are minor and that methods assuming a more polygenic architecture performed better in males whereas methods performing variable selection gave higher accuracy in females. Furthermore, the results show that incorporating functional information (GO terms) can improve prediction accuracy, emphasizing the utility of integrating biological knowledge into statistical models.

Strengths

The manuscript provides an extensive comparison of ten statistical methods. By analyzing male and female data separately, the study highlights the importance of considering sex-specific genetic architectures in predictive modeling, which is crucial for traits like starvation resistance that exhibit sexual dimorphism. The results provide a thorough evaluation of differences in prediction accuracy both within sex and between sex. Furthermore, the use of Gene Ontology (GO) annotations to inform prediction models is a significant strength. Lastly, the manuscript acknowledges limitations, such as the small sample size of the DGRP lines, which limits the generalizability of the findings.

Weaknesses

The manuscript could comment on similar assessments of statistical methods for prediction of other complex traits based on transcriptomic data and whether these studies yielded similar results. Furthermore, the manuscript mentions that these methods do not account for non-linear interactions between genes, which could be crucial for predicting complex traits with epistatic effects but does not explore potential solutions to this problem. Additionally, there are some minor issues such as typos (e.g. Lines 240, 259, 291).

Reviewer #2: In this paper by Arango et al, the authors are comparing various statistical models to predict complex phenotypic traits using gene expression data. This question is fundamental to the field as many studies often employ gene expression analysis to identify gene modules using WGCNA/GWAS/TWAS. While the authors approach a very logical question, there is more information needed to strengthen this manuscript.

Major comments:

1. The authors claim that a limitation of their data is because a sample size of ~200 cell lines (~12000 highly expressed genes) is a small sample size, which is not. ~12000 genes in each sex is a decent sample size to predict phenotypic traits, especially in Drosophilia. Thus the authors must speak a bit more on why this is a limitation in the Drosophila field.

2. The authors themselves mention that the differences in prediction accuracy between the various methods is not large (see abstract and fig 1.). Thus, even if the difference exists, why would it be important to select one method over another if the difference between methods is not large? Moreover, can the authors quantify this difference to accurately determine if the difference is significant or not?

3. In lines 240-243, the authors mention that the reason the prediction accuracy was low was because of genetic variation in the lines from individual flies. However, even when such methods are applied to human data, we are dealing with high genetic variation among the population. Then in such cases why would any of these methods be employed if their accuracy is low due to genetic heterogeneity? Importantly, for Drosophilia in fact the genetic variability will be much lower (considering the lines are inbred; see line 60) than when dealing with human traits and gene expression data. This shows that such methods will ultimately will not be able to predict such variable genotypic data with accuracy. The authors should consider commenting on this.

4. In this paper the authors select starvation resistance as the phenotypic trait, however the authors state that it is well known that sex differences exists in starvation parameters (see discussion section, line 395-398). When the prediction accuracy of the statistical analysis is low, then why not also select a trait that is not known to differ by sex. This would allow to better understand if sex drives the low accuracy of the statistical parameters or if inherently these statistical tests are poor at predicting complex traits. Thus, the authors should consider comparing the methods with another trait not dependent on sex.

Minor comments:

1. The result section is written more like a discussion section. This is especially true for Gene Analysis. Please consider re-writing this section

2. Please consider proving a concluding statement for each of your result section.

Reviewer #3: The authors have presented a comparison of various statistical learning methods to predict gene expression. the manuscript is written in a clear and concise manner. The authors though can provide basis to a few parameters used (see attached manuscript). Providing relevance and basis to the parameters used for testing will enhance the depth of their work and provide more context and usefulness to the readers.

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Attachments

Attachment

Submitted filename: PONE-D-24-23903 review.pdf

PONE-D-24-23903R1Comparing statistical learning methods for complex trait prediction from gene expressionPLOS ONE

Dear Dr. Morgante,

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 Dec 30 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,

Ashutosh Pandey, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

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 #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 #2: Yes

**********

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

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 #2: Yes

**********

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: 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 #2: The authors have decently answered the concerns I raised in the previous version. However, I still have some concerns and believe that it will strengthen the paper:

1. While the authors have answered my previous comment regarding editing the result section with their justification, I still believe the part of the results section labeled as " Gene Analysis" has much background information that is not typical to Results sections. Please consider trimming this section down to keep it to the results and move parts to discussion session especially where previous studies have been compared.

2. I feel like that the authors removed the lines in 240-243 in the previous version on the basis of it not being significant to their claim and the potential for it to be confusing (mentioned in the response to the reviewer letter), but I in fact think that it is valuable when papers provide limitations of their study as no study is perfect. My suggestion would be to add this statement back and explain with the statistical equation (provided in the response letter) that the authors put in reviewer comments which will back their reasoning and avoid any confusion.

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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 #2: No

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[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment 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. 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.

Comparing statistical learning methods for complex trait prediction from gene expression

PONE-D-24-23903R2

Dear Dr. Morgante,

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.

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Kind regards,

Ashutosh Pandey, Ph.D.

Academic Editor

PLOS ONE

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