Dear Dr Richard,

Thank you very much for submitting your manuscript "PenDA, a rank-based method for Personalized Differential Analysis: application to lung cancer" 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,

Amin Emad

Guest 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 presented a unique approach for identifying dysregulated genes in cancer data. The overall quality of the study, including writing, organization, and presentation is good. I wasn’t familiar with this type of approach, this manuscript does make a good introduction and I found it has the potential to be useful in future research studies.

Some minor comments

1. The authors summarized DEseq2 edgeR, limma to be fold-change method. These methods do provide fold change, but their algorithm is much more than just fold change.

2. Detail of how data was simulated should be provided.

3. Small grammar errors such as “Help to found”

Reviewer #2: In this interesting manuscript, Richard et al. introduce a new method, PenDA, for identifying genes that are deregulated in individual samples, based on comparison to a reference group of samples. The method works by comparing the ranking of each query gene relative to a set of genes that are consistently above or below the query gene in the reference group, and whose overall expression level is as close to the query gene as possible. The method is very simple, but in my opinion the authors have adequately shown that despite its simplicity, it performs quite well. The performance evaluation is almost entirely based on simulated data, but the simulations are performed carefully to take into account differences in behaviour of genes with varying baseline expression levels, and also to study the effect of various parameters that can be potentially tuned. The authors have then applied their method to TCGA lung cancer data, showing that their approach can identify differences between tumour and normal, can distinguish lung adenocarcinoma from squamous cell lung carcinoma, and can further identify potentially new subtypes within the adenocarcinoma cohort that correlate with genomic subtypes. Overall, I enjoyed reading the manuscript, and have only minor comments with respect to some of the details of the method evaluation and comparison:

1. In their simulations, the authors randomly choose a fraction of genes as deregulated, and modify the expression of these genes in a way that the amount of change in gene expression exceeds the natural variation that is seen in control (normal) samples (< 5th percentile or > 95th percentile). Does the choice of the percentile threshold affect the results? Can the authors perform simulations with at least one other percentile threshold (e.g. < 20th and > 80th percentiles) to show that the method performance is robust with respect to how the simulated data are constructed? Or if the performance is decreased, does the same apply to other methods that PenDA is compared to?

2. Among several different methods that the authors have evaluated, they test the ability of DESeq2 to identify deregulated genes in simulated data. My understanding from the Methods section is that the simulations are performed based on normalized data, and therefore the simulated dataset will also on the same normalized scale. However, DESeq2 requires raw read counts. Could the authors please clarify?

3. The authors show that the lung adenocarcinoma samples can be divided into three subtypes (classes I, II, and III) based on PenDA differential expression status. They state in the manuscript that a previous work could not identify these classes based on normalized counts, but it is not clear to me if this really shows the effect of PenDA vs. normalized counts, or if it reflects other potential differences in the analysis approach. Can the authors please repeat the same hierarchical clustering analysis using the same genes but with normalized counts, and compare to PenDA-based clustering?

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: 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

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, PLOS recommends that you deposit 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. For instructions see http://journals.plos.org/ploscompbiol/s/submission-guidelines#loc-materials-and-methods

Dear Dr Richard,

We are pleased to inform you that your manuscript 'PenDA, a rank-based method for Personalized Differential Analysis: application to lung cancer' 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,

Amin Emad

Guest Editor

PLOS Computational Biology

Jian Ma

Deputy Editor

PLOS Computational Biology

***********************************************************

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 answered all of my comments.

Reviewer #2: The authors have adequately responded to my comments.

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Computational Biology data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: 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