Dear Dr. Beyer,

Thank you very much for submitting your manuscript "Manuscript “Regulatory network-based imputation of dropouts in single-cell RNA-sequencing data”" for consideration at PLOS Computational Biology.

As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. In light of the reviews (below this email), we would like to invite the resubmission of a significantly-revised version that takes into account the reviewers' comments.

Please revise it according to the reviewers carefully.

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When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to the review comments and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out.

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

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Please prepare and submit your revised manuscript within 60 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email. Please note that revised manuscripts received after the 60-day due date may require evaluation and peer review similar to newly submitted manuscripts.

Thank you again for your submission. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Quan Zou

Guest Editor

PLOS Computational Biology

Ilya Ioshikhes

Deputy Editor

PLOS Computational Biology

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Please revise it according to the reviewers carefully.

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: In this manuscript, the authors proposed a transcriptional regulatory network learned from bulk gene information for single-cell RNA sequencing data imputation. The network approach showed good performance for lowly expressed genes, which could be utilized for the imputation of markers and regulators. Additionally, the authors make a point that there is no best imputation approach but only the best method for a particular combination for different circumstances. Overall, the manuscript is well organized. I have some suggestions for the authors to improve their work.

1. The authors argued that the “Baseline” method, which simply calculated the average expression value of the gene, performed well for the imputation. It is astonished that such a simple method achieved such good results. I believe that this simple method must have been reported and compared before in single-cell imputation field. I would suggest the authors to find related papers and add more discussion to this finding.

2. Hou et al. have made a terrific benchmark work for 18 single-cell RNA sequencing imputation methods:

Hou, W., Ji, Z., Ji, H. et al. A systematic evaluation of single-cell RNA-sequencing imputation methods. Genome Biol 21, 218 (2020).

Based on their conclusion an important state-of-the-art method, kNN-smoothing, was missing in this manuscript. I suggest the authors to add this method in this work.

3. The URL “http://www.tfcheckpoint.org/index.php/browse” in line 581 is not accessible, please check the link.

4. More details of previously learnt regulatory models was encouraged to be added.

5. Several abbreviations don’t have their full names for their first appearance, for instance, “MAGIC“ in line 77.

Reviewer #2: The paper aims to improve the performance of imputation on scRNA-Seq data through a transcriptional regulatory network learned from external, independent gene expression data. This study implemented an R-package called ADImpute that automatically determines the best imputation method for each gene in a dataset. The methods and results reported in this paper demonstrate that their network-based approach outperforms published state-of-the-art methods. The work proposed that imputation should maximally exploit external information and be adapted to gene-specific features, such as expression level and expression variation across cells.

This manuscript is a reader-friendly and well-written manuscript. The following concerns need to be addressed.

Major issues:

1. As far as I know, the zero values in scRNA-Seq data are partly due to dropout events and partly due to gene expression. How can the authors ensure that the method proposed to impute the dropout zeros rather than incorrectly filling gene expression?

2. The challenges of improving the imputation of scRNA-Seq should be explained in-depth, such as what are the main challenges in the study of imputation methods and how these studies do may overcome these challenges.

3. The proposed model was compared with several methods, but lack of discussion of details regarding the weaknesses. Could the authors give more analysis about why the proposed model does better than previous work? More insightful discussions should be given.

Reviewer #3: In this paper, Leote et al. proposed a regulatory network-based imputation method for dropouts in scRNA-seq data. I have the following concerns regarding this manuscript.

1. For the evaluation of imputation methods, although the authors used synthetic data and simulation, their real data analysis was rather limited. The only real data results are in the section “Network-based imputation uncovers cluster markers and regulators.” However, other commonly used downstream analyses, including clustering, DE gene analysis, and cell trajectory inference, were not evaluated on the imputed data. The authors should add more real data results to demonstrate that their imputation methods can benefit downstream analyses better than existing imputation methods do.

2. If authors want to claim that their imputation method can benefit cell marker detection, the first step should be to find meaningful cell clusters and label them as cell types. However, the cell clustering results were not shown.

3. I do not find the current real data results convincing. For example, in Figure 3A, there is little difference between the cluster markers found on the imputed data (by the proposed method) and on the original data. Hence, this figure did not make a strong point.

4. In Figure 1, can the author label the sequencing technology for the hESC data as well? It seems that the proposed network imputation favors the UMI data. If this is the case, please clarify it.

5. For Figure 2, a distance metric should be used to quantify the similarities or differences between the background and the imputed data.

6. For Figure 3, the labels for different imputation methods are not in the same order in the four panels, making it difficult for a direct comparison. Why was the original data omitted in panel B?

7. In line 465, why does a high MSE suggest better 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

Reviewer #3: None

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Figure 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. 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 us at figures@plos.org.

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Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

Dear Dr. Beyer,

Thank you very much for submitting your manuscript "Regulatory network-based imputation of dropouts in single-cell RNA sequencing data" for consideration at PLOS Computational Biology. As with all papers reviewed by the journal, your manuscript was reviewed by members of the editorial board and by several independent reviewers. The reviewers appreciated the attention to an important topic. Based on the reviews, we are likely to accept this manuscript for publication, providing that you modify the manuscript according to the review recommendations.

Please prepare and submit your revised manuscript within 30 days. If you anticipate any delay, please let us know the expected resubmission date by replying to this email.

When you are ready to resubmit, please upload the following:

[1] A letter containing a detailed list of your responses to all review comments, and a description of the changes you have made in the manuscript. Please note while forming your response, if your article is accepted, you may have the opportunity to make the peer review history publicly available. The record will include editor decision letters (with reviews) and your responses to reviewer comments. If eligible, we will contact you to opt in or out

[2] Two versions of the revised manuscript: one with either highlights or tracked changes denoting where the text has been changed; the other a clean version (uploaded as the manuscript file).

Important additional instructions are given below your reviewer comments.

Thank you again for your submission to our journal. We hope that our editorial process has been constructive so far, and we welcome your feedback at any time. Please don't hesitate to contact us if you have any questions or comments.

Sincerely,

Quan Zou

Guest Editor

PLOS Computational Biology

Ilya Ioshikhes

Deputy Editor

PLOS Computational Biology

***********************

A link appears below if there are any accompanying review attachments. If you believe any reviews to be missing, please contact ploscompbiol@plos.org immediately:

[LINK]

Reviewer's Responses to Questions

Comments to the Authors:

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

Reviewer #1: I am satisfied with the answers the authors provided to my questions. As for the newly added part of the authors (lines 73-82), I would suggest the authors to add another citation concerning technical dropout:

Zhang, Z., Cui, F., Wang, C., Zhao, L. and Zou, Q. (2021) Goals and approaches for each processing step for single-cell RNA sequencing data. Briefings in bioinformatics, 22.

Reviewer #2: The authors has answered all my questions.I have no more questions.

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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

**********

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: No

Figure 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. 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 us at figures@plos.org.

Data Requirements:

Please note that, as a condition of publication, PLOS' data policy requires that you make available all data used to draw the conclusions outlined in your manuscript. Data must be deposited in an appropriate repository, included within the body of the manuscript, or uploaded as supporting information. This includes all numerical values that were used to generate graphs, histograms etc.. For an example in PLOS Biology see here: http://www.plosbiology.org/article/info%3Adoi%2F10.1371%2Fjournal.pbio.1001908#s5.

Reproducibility:

To enhance the reproducibility of your results, we recommend that you deposit your laboratory protocols in protocols.io, where a protocol can be assigned its own identifier (DOI) such that it can be cited independently in the future. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

References:

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.

Dear Dr. Beyer,

We are pleased to inform you that your manuscript 'Regulatory network-based imputation of dropouts in single-cell RNA sequencing data' 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.

Please note that your manuscript will not be scheduled for publication until you have made the required changes, so a swift response is appreciated.

IMPORTANT: The editorial review process is now complete. PLOS will only permit corrections to spelling, formatting or significant scientific errors from this point onwards. Requests for major changes, or any which affect the scientific understanding of your work, will cause delays to the publication date of your manuscript.

Should you, your institution's press office or the journal office choose to press release your paper, you will automatically be opted out of early publication. We ask that you notify us now if you or your institution is planning to press release the article. All press must be co-ordinated with PLOS.

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

Best regards,

Quan Zou

Guest Editor

PLOS Computational Biology

Ilya Ioshikhes

Deputy Editor

PLOS Computational Biology

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