PCOMPBIOL-D-25-01122

Learning Genetic Perturbation Effects with Variational Causal Inference

PLOS Computational Biology

Dear Dr. Uhler,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology'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.

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We look forward to receiving your revised manuscript.

Kind regards,

Philipp Martin Altrock, Ph.D.

Academic Editor

PLOS Computational Biology

Marc Birtwistle

Section Editor

PLOS Computational Biology

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: The review report is uploaded as an attachment.

Reviewer #2: In this manuscript, the authors present SCCVAE, a deep generative causal inference framework designed to uncover the regulatory network underlying single-cell genetic perturbations. SCCVAE models single-cell gene expression using a structural causal model, where the intrinsic gene modules U_i are assumed to regulate each other through a linear relationship. In the experiments, the authors demonstrated that SCCVAE outperforms baselines like GEARS and scGPT in prediction of single-cell level perturbation effects. Overall the paper is well written, and the proposed method is very interesting. However, there are some questions for the authors to address for publication.

1. (testing on datasets with fewer target genes) In the manuscript, the authors primarily demonstrate the effectiveness of SCCVAE using cell line data with a large number of target genes from Replogle et al. (2022). I wonder how SCCVAE performs when the number of target genes is smaller in the training data, for example, fewer than one hundred?

2. (evaluation) For the comparison, I suggest using the Pearson correlation between predicted and true gene expression changes, instead of between expressions. This is because the correlation between the control cell and perturbed cell gene expression is already very close to 1.

3. (model training) SCCVAE incorporates an additional MMD term during training to align distributions. It would be helpful to provide more details on the implementation of this term (such as how many samples are used for its estimation during training and the choice of kernels) and to demonstrate its contribution to the prediction performance through a simple ablation study.

4. (model interpretability) The authors present the interpretability of the learned SCM in the supplementary notes. However, this is a key strength of SCCVAE that sets it apart from many other perturbation prediction models, and it should be highlighted in the main text. Additionally, it would be interesting to explore differences between the learned graph and a pre-inferred graph using methods like Causal-GSP, beyond just comparing prediction accuracy.

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: No

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Attachments

Attachment

Submitted filename: report.pdf

PCOMPBIOL-D-25-01122R1

Learning Genetic Perturbation Effects with Variational Causal Inference

PLOS Computational Biology

Dear Dr. Uhler,

Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology'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 within 60 days Nov 29 2025 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 ploscompbiol@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pcompbiol/ 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 editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. This file does not need to include responses to formatting updates and technical items listed in the 'Journal Requirements' section below.

* 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, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter

We look forward to receiving your revised manuscript.

Kind regards,

Philipp Martin Altrock, Ph.D.

Academic Editor

PLOS Computational Biology

Marc Birtwistle

Section Editor

PLOS Computational Biology

Journal Requirements:

1) We note that your Manuscript files and Supplementary Figures files are duplicated on your submission. The Supplementary figures are uploaded separately and are also part of your (SSCCVAE_revised_supplement.pdf). Please remove any unnecessary or old files from your revision, and make sure that only those relevant to the current version of the manuscript are included.

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Reviewers' comments:

Reviewer's Responses to Questions

Reviewer #1: The authors' thorough rebuttal and revision have addressed my main concerns. I have only one very minor suggestion. There is a recently published work also building causality into modern AI-type models (diffusion models) with the hope to get better generalization in comp-bio problems: https://www.nature.com/articles/s41467-025-63728-0. It is recommended to also briefly discuss this work, in terms of the differences in focus, assumptions, and settings.

Reviewer #2: In their response, the authors have addressed most of my concerns and substantially clarified the paper. However, based on the revised results, one issue remains confusing: the use of random graphs often achieves better PearsonR, which is counterintuitive. This suggests that a misspecified causal structure among gene modules might somehow improve perturbation effect prediction.

I also noticed that, in the ablation studies, the DAG was learned without sparsity constraints, whereas in the main method the authors reported that using an upper-triangular mask worked well. The authors should explain why the DAG structures are inconsistent and clarify how different choices of DAG parameterizations contribute to improving the model.

Finally, in Section 3.3.3, the statement “The error metrics (MSE, Pearson, MMD, Energy Distance) for the random graph model are significantly worse than the other models” should be revised considering the updated results.

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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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Dear Dr. Uhler,

We are pleased to inform you that your manuscript 'Learning Genetic Perturbation Effects with Variational Causal Inference' has been provisionally accepted for publication in PLOS Computational Biology.

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Best regards,

Philipp Martin Altrock, Ph.D.

Academic Editor

PLOS Computational Biology

Marc Birtwistle

Section 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: The authors have addressed my concerns.

**********

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: None

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If you choose “no”, your identity will remain anonymous but your review may still be made public.

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Reviewer #2: No