Dear Dr Villoutreix,

Thank you very much for submitting your manuscript "Partial label learning for automated classification of single cell transcriptomic profiles" 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.

The reviewers appreciate the approach presented in the paper. However, they suggest to compare it to other methods to demonstrate its superior performance. These comparisons should be included in a revised manuscript.

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Sincerely,

Stacey D. Finley, Ph.D.

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: Senoussi et al. have performed the partial label single cell annotation to overcome the herculean task of specifying the labels for each cell. They have used different machine learning (Prototype based, SVM, and KNN) methods for partial automated single cell labeling on simulated and real world datasets. They identified that non-linear prototype based works best both in simulated and real world dataset and had a comparable accuracy with the fully supervised dataset. The manuscript has been well written and analyzed well. I have some comments:

The authors have been focused on the hierarchy multi class classification which is good for developmental biology. Does the partial also perform better when there is no hierarchy in the single cell dataset?

I appreciate that the authors have detailed their method section. But they should keep the method concise in the manuscript but can keep the elaborated methods in the supplementary.

In the figures, the brown square is not properly visible for the best performing method. Please use a different color.

PB, SVM, and Knn are good methods. I would suggest authors to use Tree based methods also like Random Forest, XGBoost, and LGBM. I think it will improve the accuracy. Also please try multiclass logistic regression.

It is good that the author has used both regular SVM and kernel SVM. I am a bit surprised that K-SVM has less F1 score than the regular SVM in single cell annotation. it could be due to the hyperparameter for K-SVM is not properly optimized, since in single cell data is non-linear. The same partial annotation works well for non-linear prototype based. Please explain.

Reviewer #2: I would like to see a comparison between the performance of your program and some others because I find it very useful to see if the program works better than others.

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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

Figure Files:

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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.

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Dear Dr Villoutreix,

We are pleased to inform you that your manuscript 'Partial label learning for automated classification of single-cell transcriptomic profiles' 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,

Stacey D. Finley, Ph.D.

Section Editor

PLOS Computational Biology

Stacey Finley

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 #2: Congrats!!!

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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 #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 #2: No