Dear Prof. Kuang,

Thank you very much for submitting your manuscript "Imputation of Spatially-resolved Transcriptomes by Graph-regularized Tensor Completion" for consideration at PLOS Computational Biology.

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Sincerely,

Sushmita Roy, Ph.D.

Associate Editor

PLOS Computational Biology

Jason Papin

Editor-in-Chief

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: Reproducibility report has been uploaded as an attachment.

Reviewer #2: REVIEW IS UPLOADED AS AN ATTACHMENT.

Reviewer #3: The paper presents an approach to imputation of gene expression in spatial transcriptomics data using graph-regularised tensor completion. This is an interesting class of model for spatial data and I can imagine that it could be useful in smoothing data or in other data analysis tasks, e.g. smoothing followed by clustering may well be a good approach. I can also believe that the method performs well in the synthetic benchmarks where data are removed (at random I think?) and then imputed, compared to the other models considered here. However, it was not clear to me exactly how it should be used in real data. Are the authors suggesting that all zeros should be imputed with non-zero values? Is this what is done (I was not sure) and is that justified? The zeros are not missing-at-random data, they are data that happens to be zero usually because expression levels are low and read-depth is limited. Replacing them with non-zero values seems to me to be more akin to smoothing than to imputation. It is therefore not clear to me that this is justified. Smoothing may be useful prior to modelling (e.g. clustering) but that is not the same as imputation which should really only be used to deal with missing data, not low counts data. I'm afraid I therefore don't agree with the way the method is presented as an imputation approach. I have two specific major concerns:

Major comments:

[1] It is not clear in modern UMI-normalised data whether (or when) zeros should be considered artefacts to be corrected/imputed since they may simply reflect genuinely weak expression. If the expression is weak then imputation should not be used and will smooth out genuine signal in the data. If genes are weakly expressed then counts can be very low and zeros simply reflect read depth limitations. For example, in the case of single-cell RNA-Seq data recent work shows that UMI-normalised data is not highly zero-inflated [1,2,3] and this was really only a problem on older datasets without UMI normalisation where amplification artefacts were harder to remove. I imagine the same holds true for UMI-normalised spatial transcriptomics data, in which case imputation of zero-valued genes is not justified. The citations in the introduction discussing modelling zero-inflation (Refs [22, 25, 26. 27] in the paper) predate these more recent works and often model zero-inflation as an artefact to be corrected for rather than simply part of a standard measurement model such as a negative binomial distribution over counts.

[2] Another major concern I have it that PPI data is used to regularise genes on the basis that interacting genes are more likely to be correlated in gene expression. I was wondering how good is this assumption since it seems very easy to check with data. Another approach would be to use previous expression datasets to assess co-expression, for example, since expression data are so plentiful. Is PPI the best choice for determining whether genes are likely correlated? This could be tested on observed expression data, e.g. how much co-expression is explained by PPI data?

References

[1] Choi, K., Chen, Y., Skelly, D. A., & Churchill, G. A. (2020). Bayesian model selection reveals biological origins of zero inflation in single-cell transcriptomics. bioRxiv.

[2] Svensson, V. (2020). Droplet scRNA-seq is not zero-inflated. Nature Biotechnology, 38(2), 147-150.

[3] Sarkar, A. K., & Stephens, M. (2020). Separating measurement and expression models clarifies confusion in single cell RNA-seq analysis. BioRxiv.

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: None

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

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Attachments

Attachment

Submitted filename: Reproducible_report_PCOMPBIOL_D_20_01355.pdf

Attachment

Submitted filename: review.docx

Dear Prof. Kuang,

Thank you very much for submitting your manuscript "Imputation of Spatially-resolved Transcriptomes by Graph-regularized Tensor Completion" 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. 

In particular please address reviewer 2's comment.

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,

Sushmita Roy, Ph.D.

Deputy Editor

PLOS Computational Biology

Jason Papin

Editor-in-Chief

PLOS Computational Biology

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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: Reproducibility report has been uploaded as an attachment.

Reviewer #2: The authors have satisfied my concerns, they have:

1) satisfied my request of broad applicability by performing "additional experiments on the spatial transcriptomics datasets from 3 replicates of mouse tissue (olfactory bulb) provided by Stahl et al. (2016)."

2) clarified and articulated the imputation problems. They performed both 1) spot-wise imputation (newly added) and 2) gene-wise imputation experiments.

3) added additional information to run the scripts. "We provided the scripts to display the key results reported in the paper."

4) clearly highlighted the scalability of the method. "we also included the space complexity of FIST together with the time complexity to justify that FIST is a scalable methods without need of computation with the full CPG."

One more minor point that does not need to return to me for acceptance:

- In the abstract it reads: "FIST significantly outperformed several best performing single-cell RNAseq data imputation methods."

"several best performing methods" sounds like an oxymoron. There is only one best performing method, not several.

Reviewer #3: The authors have addressed all my comments in the revised version

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Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

Reviewer #3: 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: Yes: Anand K. Rampadarath

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.

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, PLOS recommends that you deposit 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. For instructions see http://journals.plos.org/ploscompbiol/s/submission-guidelines#loc-materials-and-methods

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.

Attachments

Attachment

Submitted filename: Reproducible_report_PCOMPBIOL_D_20_01355R1.pdf

Dear Prof. Kuang,

We are pleased to inform you that your manuscript 'Imputation of Spatially-resolved Transcriptomes by Graph-regularized Tensor Completion' 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.

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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,

Sushmita Roy, Ph.D.

Deputy Editor

PLOS Computational Biology

Jason Papin

Editor-in-Chief

PLOS Computational Biology

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