Dear Dr Tjärnberg,

Thank you very much for submitting your manuscript "Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data" for consideration at PLOS Computational Biology.

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Sincerely,

Qing Nie

Associate Editor

PLOS Computational Biology

Weixiong Zhang

Deputy 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: This paper presents an interesting approach to denoising single-cell RNA-seq data based on (a) construction of a kNN graph (after PCA dimensionality reduction), (b) diffusion based denoising and (c) noise2self self-supervision procedure to select hyperparameters in this procedure: (i) dimension of PCA embedding, (ii) number of neighbors in the kNN graph, and (iii, presumably) number of diffusion steps in denoising.

The paper is well written, with appropriate benchmarking, analysis and interpretation. The method itself provides benefits in analysis due to the ability to provide a procedure for parameter selection based on self-supervision.

Nonetheless it would be helpful if the manuscript addressed some issues:

(a) comparison with noise2self pipeline for analysis. Some of the analysis of this paper mirror analyses from the noise2self paper (eg, the comparison of marker expression in optimal denoising vs. over smoothing). With that in mind, it would be helpful to see a comparison of the proposed algorithm with noise2self.

(b) the downstream analysis (variance retained) section is very limited. For instance, I don't follow how the authors use the number of PCs to recapture total variance after denoising as an argument for better performance. Please clarify in text.

(c) a more thorough analysis of differential expression can be performed beyond showing the effect on just a small number of marker genes. An analysis similar to the benchmark from Hou et al. https://www.biorxiv.org/content/10.1101/2020.01.29.925974v1 would be helpful.

(d) this is minor, but there is a statement that any kNN graph can be used but no experiment on the effect of the algorithm used to construct the kNN graph is provided. The UMAP method is fairly sophisticated and I wonder how much of the results presented here are based on how well that construction is made. Perhaps, a test with at least one other construction method would be helpful.

Reviewer #2: This is an overall well-structured paper proposing data imputation and denoising in single-cell transcriptomic data. The imputation is based on learning cell-cell similarity. It used the expression of a gene on neighboring cells of the cell of interest to impute the expression level of the same gene on this cell. The authors outlined their computational algorithm and use real scRNA-seq datasets to demonstrate its utility and compared with some existing imputation methods. My comments are as follows:

1. In the algorithm description on pages 10 and 11, Equations (2) and (5) have typo. Subscript k does not appear on the right side of both equations.

2. It is unclear the benefit of setting the diagonal elements of the Markov matrix M to 0. How does this step impact the performance in the real data analysis? Apparently, MAGIC does not require this step. The authors can compare their algorithm performance with and without this step.

3. There are many imputation methods available in literature. The authors compared the performance of their method to DrImpute and SAVER in the assessment of cell-cell correlation, and to DeepImpute and MAGIC in cell type clustering. In the last experiment to assess data variance, the authors compared to MAGIC. There is lack of comprehensive comparisons. I suggest to add a few more existing methods and compare them with the new one in every experiments outlined in this paper.

4. What is the computation cost? Please benchmark the computation time in comparison to other existing methods.

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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

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Reviewer #1: No

Reviewer #2: No

Figure Files:

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Dear Dr Tjärnberg,

Thank you very much for submitting your manuscript "Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics 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,

Qing Nie

Associate Editor

PLOS Computational Biology

Weixiong Zhang

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 appreciate the response from the authors and see all my questions addressed. While I recommend acceptance, better presentation of the new benchmark results would be great.

Reviewer #2: Most of my previous comments were addressed to my satisfaction except my first comment.

In Eq. (1), the matrix M represents cell-to-cell similarity and is a right-stochastic matrix with each row summing to 1. In the proposed new algorithm, the imputation was done by calculating the weighted average of express levels of the same gene in neighboring cells to recover the gene expression in a cell. So in Eq. (2), the imputed value of gene j in cell k is calculated by ∑_(k'=1)^z〖u_kk' x_k'j 〗, where the weight u_kk' is the kk'-th element of M and represents the similarity between cells k and k'. Additionally, the constrain on row sum of M was wrongly presented as column sum in the manuscript. Of course, if M is a square matrix, it would have the same constrains on row sum and column sum. Follow the similar argument, the subscripts in Eq. (5) also contained errors.

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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

**********

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, 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

Attachments

Attachment

Submitted filename: comments.docx

Dear Dr Tjärnberg,

We are pleased to inform you that your manuscript 'Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics 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,

Qing Nie

Associate Editor

PLOS Computational Biology

Weixiong Zhang

Deputy Editor

PLOS Computational Biology

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