Dear Paulevé,

Thank you very much for submitting your manuscript "scBoolSeq: Linking scRNA-Seq Statistics and Boolean Dynamics" 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.

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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,

Mark Alber, Ph.D.

Section Editor

PLOS Computational Biology

Mark Alber

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: Authors propose a novel method for binarizing scRNAseq data to generate a Boolean representation of experimental data. These approaches are crucial for enhancing dynamic modeling of genetic regulatory networks by formalizing data representation. They compare their method to another, presenting advantages. The techniques appear sound, and I believe the work is valuable and merits publication after addressing some queries and making necessary revisions.

Questions:

. Is the classification of scRNAseq data into only three categories sufficiently general?

. The "zeroinf" case appears to be the most noisy. Are the "?" cases discarded? It would be beneficial to observe cases that do not fit into these three categories and how the authors handle them.

. In the second paragraph of the section 'From pseudocounts to...': Can the authors devise a method in scBoolseq to reject a dataset if it is inadequate for binarization? Otherwise, users may employ it in inappropriate cases.

. I was able to install the software without problems, however I did not test it with data.

. A tutorial on using scBoolSeq would be highly beneficial.

. Can an inferred Boolean network be exported from the software to be used in another Boolean tool?

Minor:

Line 38: "active"

Line 57: Please define 'GRN'.

Line 86: "HVGs"

Line 355: Provide a reference for 'k-nearest-neighbors classifier'.

Line 356: Provide a reference for 'by-category bijective matching'.

Please cite the link 'github.com/colomoto/colomoto-docker' for scBoolSeq, as other Colomoto webpages in the web appear to be outdated.

Reviewer #2: In this manuscript, López et al. present scBoolSeq, a method to link scRNA-seq data with Boolean network models by learning gene expression distributions to binarize data or simulate realistic data from Boolean states.

The method addresses an important need and the manuscript is generally well-written. However, the below points should be addressed to strengthen the work. In particular, further evaluation of the binarization results and discussion of computing requirements would help users assess the method's applicability. With these issues addressed, the work will be a valuable contribution to the field.

Major points:

1) How does the binarization model treat housekeeping genes that are constitutively active? By looking at the unimodal example in Figure 3, it seems that these genes will have 3 states instead of being in the 1 state.

2) How do you treat unimodal genes that are not symmetric? Symmetric thresholds may be suboptimal.

3) Regarding the statement on lines 120-121, "The log transformation is necessary in order to ensure the validity of the underlying parametric distributions", please consider briefly expanding on this.

4) Lines 116-117 state "...would represent the standard library size normalisation, yielding counts/reads per million (CPM/RPM)." This is not correct; a multiplicative factor of 1E6 is needed.

5) The text states, "In a first step, genes which do not exhibit a high enough variability". Please define "high enough" quantitatively.

6) Regarding the statement on lines 411-412, "Future engineering work will focus on leveraging the AnnData [59] Python package for handling large datasets that cannot be fit in RAM," please expand on the computing requirements and maximum dataset size that can be analyzed.

Minor points:

It is better to spell "scRNA-Seq" as "scRNA-seq" throughout the manuscript.

On line 127, fix the typo: "patters" -> "patterns".

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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

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Reviewer #1: No

Reviewer #2: No

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Dear Paulevé,

We are pleased to inform you that your manuscript 'scBoolSeq: Linking scRNA-Seq Statistics and Boolean Dynamics' 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,

Christoph Kaleta

Section Editor

PLOS Computational Biology

Mark Alber

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 manuscript is now quite clear.

Reviewer #2: The authors did a great job in addressing all the 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: Yes

Reviewer #2: 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: Yes: José Carlos Merino Mombach

Reviewer #2: No