Dear Associate Professor Waldron,

Thank you very much for submitting your manuscript "Curated Single Cell Multimodal Landmark Datasets for R/Bioconductor" 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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[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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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,

Mingyao Li

Academic Editor

PLOS Computational Biology

William Noble

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 authors collected multimodal single-cell datasets from multiple human tissues and build a resource named SingleCellMultiModal. The landmark datasets and the Bioconductor package presented here will be useful for new computational method development. However, I have two concerns about the manuscript.

1. Collecting the right datasets is important for computational method development. One challenging point is that most datasets do not have cell type label information. Thus, it is difficult to evaluate the newly developed methods. There are some datasets that have great quality and have been analyzed in literatures by some great methods or have been manually annotated. Collection of those datasets will be extremely useful for method development. The impact of the software/manuscript will be significantly increased if datasets with annotated cell labels will be included.

2. Because of technical reason, there are always have some bad quality cells. Including too much bad quality cells will affect the performance of the computational methods. It would be helpful for users if a reasonable quality score is included.

Reviewer #2: The manuscript ‘Curated Single Cell Multimodal Landmark Datasets for R/Bioconductor’ described an effort to compile landmark datasets from various of single-cell multi-modal technologies in one place. Specifically, the authors created an R/Bioconductor package, SingleCellMultiModal that allows the easy access to these datasets. This is a valuable resource for method developer and for experimentalist who might be thinking about generating the multi-modal datasets.

Major concern:

1. For method developers who want to test the method using a publicly available dataset, they will need to have the ground-truth annotation associated with the data. For most of the datasets described in the manuscript, a common ground-truth information one would need is the cell type annotation. It’d be clearer if the authors have more explicit description on what metadata is provided for each dataset.

2. For some of the data types, such as 10X multiome and seqFISH datasets, there are additional datasets critical for the analysis. For example, the fragment file (listing the open fragment region sequenced per cell) for 10X multiome, and the fluorescence images for seqFISH. How would these data be distributed to the users?

3. Currently, it is difficult to conceptualize how an object from the SingleCellMultiModal package looks like. I think adding a more detailed description and potentially a graphical illustration of the object under the ‘Summary of landmark datasets in SingleCellMultiModal’ heading will be beneficial for readers.

Minor concern:

1. For Figure 1A and 1B, enlarge the font. Currently, I need to zoom in quite a lot to see the text in the figures. For the x-axis labels, could enlarge the font and rotate them to avoid stacking.

2. There is a typo in the Figure 4 legend. Should be ‘(right) pre-processing steps of source data’.

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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: Yes: Zhana Duren

Reviewer #2: No

Figure Files:

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Dear Associate Professor Waldron,

We are pleased to inform you that your manuscript 'Curated Single Cell Multimodal Landmark Datasets for R/Bioconductor' 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,

Mingyao Li

Academic Editor

PLOS Computational Biology

William Noble

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 have addressed all my comments and I have no further comments.

Reviewer #2: Thanks for answering all the comments. I do not have further questions. Great work!

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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: Zhana Duren

Reviewer #2: Yes: Michelle Y. Y. Lee