PCOMPBIOL-D-24-01365Identifying patterns differing between high-dimensional datasets with generalized contrastive PCAPLOS Computational Biology Dear Dr. Sjulson, Thank you for submitting your manuscript to PLOS Computational Biology. After careful consideration, we feel that it has merit but does not fully meet PLOS Computational Biology's publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process. Please submit your revised manuscript within 60 days Jan 04 2025 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at ploscompbiol@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pcompbiol/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. Please include the following items when submitting your revised manuscript: * A rebuttal letter that responds to each point raised by the editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. This file does not need to include responses to formatting updates and technical items listed in the 'Journal Requirements' section below.* A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.* An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'. If you would like to make changes to your financial disclosure, competing interests statement, or data availability statement, please make these updates within the submission form at the time of resubmission. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. We look forward to receiving your revised manuscript. Kind regards, Stefano PanzeriAcademic EditorPLOS Computational Biology Lyle GrahamSection EditorPLOS Computational Biology Feilim Mac GabhannEditor-in-ChiefPLOS Computational Biology Jason PapinEditor-in-ChiefPLOS Computational Biology  Journal Requirements: Additional Editor Comments (if provided):   [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: Uploaded as an attached pdf

Reviewer #2: In this paper, the authors propose a variant of contrastive PCA, an recently-developed algorithm that identifies differing patterns between two contrasted datasets. The authors' contribution is a set of algorithms that are parameter-free unlike the original algorithm and also more intuition into the outputs of the original algorithm its limitations and suggestions on how to overcome these. Application of these algorithms to three datasets showcases their features well. I found the paper informative, insightful, and well-written and have a few suggestions that could strengthen its impact and usability:

a) I found the distinction that the authors provide between discriminant analyses and cPCA very useful and intuitive for prospective users of the methodology. I wonder if the authors could also apply LDA-like analyses to one of the datasets they used here and compare/contrast the results with gcPCA to offer more intuition into these differences. This would be very informative for the readers to appreciate the usefulness of cPCA and gcPCA in particular here and understand when these are more suitable for their analyses over LDA.

b) Can we get some more intuition into what constitutes "infinite data" in this context? Is there a threshold that the authors have empirically determined? Or are all actual datasets finite?

c) Some more intuition into when orthogonality or sparsity would be useful in this context would help too. For example, what outputs would the orthogonal or sparse gcPCAs give for one of the examined datasets? How would these differ from the unconstrained ones? Are there other caveats in including such constraints, e.g. algorithm convergence or complexity?

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Reviewer #1: None

Reviewer #2: Yes

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Reviewer #1: No

Reviewer #2: Yes: Ioannis Delis

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Attachments

Attachment

Submitted filename: gcPCA Review.pdf

Dear Dr. Sjulson,

We are pleased to inform you that your manuscript 'Identifying patterns differing between high-dimensional datasets with generalized contrastive PCA' 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.

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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,

Stefano Panzeri

Academic Editor

PLOS Computational Biology

Lyle Graham

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: The new figures in the supplement are useful additions to the manuscript

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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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Reviewer #2: Yes: Ioannis Delis