PCOMPBIOL-D-25-01609

DAGFormer: A graph-based domain adaptation approach for single-cell cancer drug response prediction

PLOS Computational Biology

Dear Dr. Huang,

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,

Wei Li, Ph.D.

Academic Editor

PLOS Computational Biology

Pedro Mendes

Section Editor

PLOS Computational Biology

Journal Requirements:

1) Please ensure that the CRediT author contributions listed for every co-author are completed accurately and in full.

At this stage, the following Authors/Authors require contributions: ZhiHua Du, Fen Yan, and Yu-An Huang. Please ensure that the full contributions of each author are acknowledged in the "Add/Edit/Remove Authors" section of our submission form.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Authors:

Please note that one review is uploaded as an attachment.

Reviewer #1: The manuscript by Yan, Du, and Huang presents DAGFormer, an graph-based domain adaptation framework for integrating bulk RNA-seq and scRNA-seq data to predict cancer drug responses at the single-cell level. The prediction of single-cell drug response is crucial for advancing personalized cancer therapy, and the challenges of integrating heterogeneous data sources (bulk vs. single-cell) are well-recognized. The manuscript is well-written and clearly articulates this significance. I recommend acceptance after revisions, as outlined below.

1. In Figure 1, the bulk RNA-seq dataset (Table 1) is used as the source domain and scRNA-seq datasets (Table 2) as the target domain. Please clarify whether the scRNA-seq data are used exclusively for evaluation or also involved during training.

2. The overall loss function includes several weighting coefficients. Please provide details on how these hyperparameters were selected (e.g., grid search, prior work, or manual tuning). In addition, specify the computational environment (e.g., GPU model, hardware configuration) to support reproducibility.

3. Figure 5 visualization: The color scheme of Figure 5 could be improved for consistency and readability. A unified and aesthetically optimized palette would enhance clarity.

4. Minor language issue: On page 3, line 76, “Bulk RNA-seq” is capitalized incorrectly. For consistency, please revise to “bulk RNA-seq” (lowercase, unless at the beginning of a sentence).

Reviewer #2: Uploaded as an attachment

Reviewer #3: The authors propose a Graph-based Domain Adaptation framework that integrates bulk and scRNA-seq data for predicting single-cell drug responses. The DAGFormer builds cell–cell graphs for bulk and single-cell expression (via SRCC, PCC, or KNN), passes them through private (domain-specific) and shared encoders with a graph-transformer backbone to reconstruct graphs and extract the multi-level features, and aligns domains with adversarial (GDA) domain adaptation while predicting single-cell drug response with a trained predictor. Benchmarking DAGFormer on ten independent scRNA-seq datasets demonstrated its superior performance compared to existing methods. However, there are still some limitations.

Major issues:

Q1. There are several hyperparameters for constructing graphs (like SRCC threshold, individual weights in the objective) in the DAGFormer; the current manuscript lacks robustness evaluation, which can reflect the influence of those hyperparameters on the performance of the proposed model.

Q2. The author needs to provide the specific definition of the loss function in the discriminator for the source domain and the target domain.

Q3. There are many models developed in the past two years, but the authors only selected scDEAL, SCAD, and some general machine learning algorithms as baseline methods. It is confusing, and the conclusion would be more comprehensive and reliable if the author compared the proposed model with a more recently developed method.

Q4: The introduction states DAGFormer is "the first DL model" to explore multiple cellular-graph topologies for single-cell drug response with bulk integration; unless a comprehensive survey confirms this, the phrasing should be softened.

Q5: Please provide a baseline protocol table, including versions, seeds, epochs, learning rates, etc.

Q6: The graph construction processing is time-consuming; the author should discuss the proposed models' time complexity and storage burden.

Minor issues:

Q1: The author needs to provide more details about the datasets in the article.

Q2: The GitHub page needs to be improved, and a tutorial is also necessary.

Q3: There are some errors in equation (20), especially the character "where" should not be indented two spaces to the right at the beginning of the paragraph.

Q4: The author needs to improve figure clarity (fonts, resolution, consistent labels).

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Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: Yes: Yangqi Su

Reviewer #3: No

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Attachments

Attachment

Submitted filename: Review of Manuscript DAGFormer.docx

Dear dr. Huang,

We are pleased to inform you that your manuscript 'DAGFormer: A graph-based domain adaptation approach for single-cell cancer drug response prediction' 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.

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Thank you again for supporting Open Access publishing; we are looking forward to publishing your work in PLOS Computational Biology. 

Best regards,

Wei Li, Ph.D.

Academic Editor

PLOS Computational Biology

Pedro Mendes

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 #1: The authors have revised the manuscript accroding to my comments.

Reviewer #2: Uploaded as an attachment

Reviewer #3: The issues mentioned in the first round review have been addressed.

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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: None

Reviewer #2: Yes

Reviewer #3: Yes

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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: Yes: Yangqi Su

Reviewer #3: No

Attachments

Attachment

Submitted filename: PCOMPBIOL-D-25-01609-R1-response.docx