Dear Prof. Zhao,

Thank you very much for submitting your manuscript "Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders" for consideration at PLOS Computational Biology.

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Sincerely,

Mingyao Li

Associate Editor

PLOS Computational Biology

Jian Ma

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: Please see attached file.

Reviewer #2: In this manuscript, Wang and Zhao propose scAAnet, a deep autoencoder for non-linear archetypal analysis of single-cell RNA-seq data. Building on the assumption that the expression profile of each cell results from a non-linear combination of multiple gene expression programs (GEPs), scAAnet aims to detect GEPs and infer the relative activity of each GEP across cells. There are many merits in this work. The manuscript is well organized and clearly written. The methods are innovative and clearly presented, based on conceptually reasonable assumptions. The simulations were carefully designed and novel biological insights revealed from applications to several real datasets.

I have two relatively major comments. First, scAAnet assumes that cell types share some GEPs. While this is conceptually justifiable, can the authors quantify the level of shared GEPs in real data, or evaluate how would scAAnet perform if the cells don’t share GEPs in the extreme case? Second, scAAnet needs to preset archetype. It is unclear how to preset the values. How would scAAnet’s performance vary with different hyperparameter choices. In the pseudo codes, there seems a warm up period where the Z^fix will be updated simultaneously. However, if that is the case, it seems that Eq (3) on page 32 is no longer identifiable?

I have some additional minor comments.

1. Can GEPs be used to infer cell types? Would it be possible to use the inferred mixture of GEPs to infer cell type?

2. GEPs identified in real data show their values in revealing underlying latent factors, pathways or network. I assume other scRNA-seq methods can also be used for similar purposes, in more convoluted manners, for example, by first clustering the cells and then performing analysis based on cell type differentially expressed genes? Presumably, without the meaningful reduction into GEPs, alternative methods that do not summarize data into GEPs would provide much noisier conclusions? The authors can consider trying competing methods on some of their real datasets.

3. The authors showed that shared pattern across cell types/disease can be obtained from inferred GEPs. But we can also analyze the cell type marker genes/ disease marker genes and compare the similarity of marker genes in different cell types/ diseases. What will be the benefit of scAAnet if the GEP analysis eventually goes back to GEP marker gene analysis?

Reviewer #3: The paper proposes an interesting model for understanding the gene expression programs underlying cells from a population with continuous cell states. The nonnegativity and convexity required in archetypal decomposition enable ease of interpretation. Compared with linear archetypal decomposition by NMF, scAAnet incorporates nonlinearity naturally through an autoencoder.

- The paper has discussed several conceptual advantages of scAAnet including nonlinearity and the inclusion of the archetypal constraint. While it is clear the method achieves better results on the simulated data, I was wondering if some comparison can be performed on any of the real data examples, in particular with NMF based methods. Does the nonlinearity allow scAAnet to capture different biological signals? Some comparison will help practitioners better understand the utility of the method, given scAAnet comes with additional computational cost.

- The paper has explained how to choose K, the number of archetypes, through stability criteria. How robust is the method to actually misspecified K? It is a point worth investigating at least on simulated data.

- The archetypal constraint assumes all cells lie in a tight convex hull in the latent space, which seems a bit restrictive to me. For example, many developmental data have trajectories with bifurcation structure. It would be great if the authors could provide more discussion on the feasibility of this assumption.

- Related to the question above, one could imagine increasing K or using a larger convex hull would accommodate for more general shapes. In this sense, is there some trade-off involved in the use of archetypal constraint loss? Usually when different losses are combined, they are weighed differently with tuning parameters. Does the relative weight of reconstruction loss vs. archetypal loss affect any part of the results?

- It is stated that in simulation 50% of the samples were generated from the boundary of the simplex, which seems a bit contrived. What is the purpose of this construction? Would it be more realistic to include some outliers outside this simplex?

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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: No: The method is on GitHub but "Analysis codes for generating results in this paper are available upon request by email." It would be good if these are published as well.

Reviewer #2: Yes

Reviewer #3: Yes

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Reviewer #1: No

Reviewer #2: No

Reviewer #3: No

Figure Files:

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Attachments

Attachment

Submitted filename: R1.pdf

Dear Prof. Zhao,

Thank you very much for submitting your manuscript "Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders" 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,

Mingyao Li

Associate Editor

PLOS Computational Biology

Jian Ma

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: The authors have addressed most of my comments. I would like to see a few supplementary figures added to improve the interpretation of the GEPs.

Specifically, regarding the inferred GEP matrix. The GEPs corresponds to the archetypes, which, as the authors describe, represent “pure types or extrema of the real data”.

In the pancreatic data set, most cell types are primarily associated with one GEP, to the extent that we might even annotate that GEP using the cell type name. Epsilon cells are associated with GEP 3 (gamma) and GEP 5(delta), and the authors provide the interpretation that Epsilon cells are similar to both gamma and delta cells.

How does one interpret that the beta cells having very high usage of either GEP2 or GEP7? The authors compared GEP2 and GEP7 and concluded that these contain different top genes, and the genes are associated with different GO terms. Does this suggest that there are two different subtypes of beta cells, one subtype resembling archetype 2 and another resembling archetype7?

The authors provide in Fig 10 the top 20 DEGs for GEP2 and GEP 7. But lacking an overlap between the top 20 genes does not directly reflect how much GEP2 and GEP7 differ. Can the authors provide a heatmap for the GEP matrix (as described on Line 178-179)? The usage of the GEPs are visualized (for example, in Fig 5c). There are 2000 HVGs from 1937 cells, so the GEP matrix is not much larger than the GEP usage matrix. A heatmap can provide a rather straightforward view of the GEPs, and help the reader interpret the GEPs, in addition to the top DEG and GO analysis that the authors already provide.

It would be useful to add these in supplementary figures:

• For the simulated data, heatmaps of the true GEP matrix and the inferred GEP matrix(after re-arranging the GEPs to best match the true order)

• For the real data sets, heatmaps of the inferred GEP matrix

Minor:

The DEG test for archetypes

Line 799 : is $m_i$ the library size? To be consistent with the notation used earlier, maybe it’s better to use $l_i$ again.

In the results for the lung fibroblast and myofibroblast cells: “GEP 3 is the one not only cell-type-specific but also disease-related.” However, Fig 7 seems to suggest that in Myofibroblasts, both GEP2 and GEP3 usage are disease related?

Reviewer #2: The authors have carefully addressed all my comments. I have no additional suggestions.

Reviewer #3: The authors have addressed all my comments.

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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

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: No

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, we recommend that you deposit your 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. Additionally, PLOS ONE offers an option to publish peer-reviewed clinical study protocols. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols

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 Prof. Zhao,

We are pleased to inform you that your manuscript 'Non-linear archetypal analysis of single-cell RNA-seq data by deep autoencoders' 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.

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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,

Mingyao Li

Associate Editor

PLOS Computational Biology

Jian Ma

Deputy Editor

PLOS Computational Biology

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