Dear Dr. Zheng,

Thank you very much for submitting your manuscript "Secuer: ultrafast, scalable and accurate clustering of single-cell RNA-seq 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.

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[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

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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,

Piero Fariselli

Academic Editor

PLOS Computational Biology

Lucy Houghton

Staff

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 a bipartite graph based clustering algorithm for single-cell RNA-seq clustering. This work is similar to the U-SPEC algorithm in [23]. Please provide more detailed and specific discussions about the similarity and dissimilarity between the proposed algorithm and the U-SPEC algorithm. Experimental comparison between the proposed algorithm and the U-SPEC algorithm is also suggested.

In the experiments, the benchmark datasets and the baseline algorithms should be described with more details. An additional table of their statistics can help improve the clarity.

In the reference [23], multiple U-SPEC clusterers are jointly modeled via an ensemble clustering strategy. Similarly, some ensemble clustering techniques, such as multidiversified ensemble clustering (MDEC) and enhanced ensemble clustering via fast propagation of cluster-wise similarities, can also be considered in the future extensions.

Reviewer #2: The authors propose a sound and elegant clustering approach that shows notable (time and memory) efficiency for massive data while remaining accuracy-wise competitive with state-of-the-art alternatives. To this end, Secuer relies on simplistic yet highly effective principles: pivoting of anchors for scaling up efficiency, anchor neighborhood per observation, locally-scaled Gaussian kernel on the neighborhoods to better capture observation-to-anchor similarity, and automated estimation of the number of clusters.

The manuscript is written with outmost clarity, simplicity, and rigor.

The experiments are comprehensive and appropriate to assess the bold claims. Comparison against state-of-the-art approaches, as well sensitivity analysis on the parameters, is conducted across a large base of dataset, including diverse scRNA-seq datasets, complementary large real-world datasets, well-annotated benchmarks, and synthetic data.

The software is provided as an open-source module in GitHub, ensuring the reproducibility of the acquired results.

Some minor-moderate concerns:

1) consider moderating a few claims:

- "Secuer enjoys reduced runtime and memory usage by orders of magnitude" -> over one order of magnitude for datasets with more than 1 million cells

- "again greatly improves"

2) it is not always clear what is the default estimator to approximate the number of clusters in Secuer,

please clarify whether you opted to use the Louvain estimator on the anchors or the near-zero eigenvalue method. It is also not completely clear whether the later method was assessed.

In addition, consider disclosing in the overview the selected community detection method, as well as adding reference [11] after "near-zero eigenvalues of the graph Laplacian (see Materials and Methods)".

3) to fully attest the scalable nature of the proposed approach, consider further assessing whether Secuer can be parallelized/distributed and, if so, introducing high-level principles to this end

4) the intuition for the locally-scaled Gaussian kernel is a simple one, please briefly provide it in the Overview instead of referencing "Materials and Methods"

5) "The preprocessing involves four steps: 1) gene/cell filtering; 2) normalization; 3) selection of highly variable genes; 4) dimension reduction by PCA" -> please ensure that the applied preprocessing parameters are available for all the tested datasets as they critically impact experiments

6) minor notes on complexity majorants:

- (2) -> please introduce d and N right after or at the beginning ('Let d and N be...')

- (1/2) -> provide the intuition for the squared root of 'p' in the Materials and further disclose the meaning of 'o' parameter

7) proof-check for minor language aspects:

- method Secuer for -> method, Secuer, for

- Fig1F -> Fig 1F

- "It appears that" -> consider instead referencing memory 'estimates' to avoid 'appears'

- "divide the graph into disjoint subgraphs" -> please validate the use of 'disjoint'

8) few notes on the supplementary material

- reformat S1

- no information is provided on the simulated datasets in S3, please clarify the generation procedure

- consider starting from a lower number of solutions in S7 to better assess the impact of consensus

I hope these comments are useful and wish to see your work published in the near future,

Kind regards

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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

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Reviewer #1: No

Reviewer #2: No

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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. Zheng,

We are pleased to inform you that your manuscript 'Secuer: ultrafast, scalable and accurate clustering of single-cell RNA-seq 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,

Piero Fariselli

Academic Editor

PLOS Computational Biology

Lucy Houghton

Staff

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 well addressed my concerns.

Reviewer #2: Thank you for the undertaken care and effort on revising the manuscript in accordance with the few suggestions,

The authors have successfully addressed my concerns

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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: None

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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: No