Two-step mixed model approach to analyzing differential alternative RNA splicing

Li Luo; Huining Kang; Xichen Li; Scott A. Ness; Christine A. Stidley

doi:10.1371/journal.pone.0232646

Peer Review History

Original SubmissionApril 17, 2020
18 May 2020 Decision Letter - Zhaohui Qin, Editor PONE-D-20-11155 Two-Step Mixed Model Approach to Analyzing Differential Alternative RNA Splicing PLOS ONE Dear Dr. Kang, Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’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. We would appreciate receiving your revised manuscript by Jul 02 2020 11:59PM. When you are ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file. If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. 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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. We look forward to receiving your revised manuscript. Kind regards, Zhaohui Qin, PhD Academic Editor PLOS ONE Journal Requirements: When submitting your revision, we need you to address these additional requirements. 1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf [Note: HTML markup is below. Please do not edit.] Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 3. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 4. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The authors applied a two-stage hypothesis testing procedure for testing differentially expressed or spliced isoforms, within a linear mixed model setting. The novelty of the methodology part is limited, as this work can be regarded as an extended application to RNA-seq data, compared to Heller (2009) and Li (2014) where the two-step testing procedure was originally proposed. Nevertheless, the data experiments of this paper were solid and complete, allowing this paper to be a good application report with interesting scientific findings. Minor comment: It is appreciative that the authors provided code for this work. A gentle advice would be providing a demonstration of applying the R functions (i.e. pipeline), rather than the functions only. Reviewer #2: The authors propose to use a linear mixed model to simultaneously assess genes with differentially expressed or spliced isoforms. A two-step hypothesis testing procedure is proposed to select the significant genes and isoforms, which controls the gene-level overall false discovery rate. The proposed method is novel and enjoys some advantages over the existing methods. The authors further demonstrate the performance of the proposed method using two real data sets and a thorough simulation study. I only have one comment: the validation of the proposed method relies on the validity of the linear mixed model (1), where the covariance matrix of y has equal off-diagonal elements in (2). Some comments are needed for the validation of such a model. If the assumption of the covariance matrix is violated in applications, the model (1) is not applicable. Is the proposed method robust to use in such a scenario? What modification is need? ****** 6. 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 [NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.] 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. Registration is free. 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. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0232646.r001
Revision 1
6 Sep 2020 Author Response PONE-D-20-11155: “Two-Step Mixed Model Approach to Analyzing Differential Alternative RNA Splicing” Dear Dr. Qin, Thank you for reviewing our manuscript. We have made revisions in response to the reviewers’ comments. A marked-up copy of the manuscript highlighting the changes as well as a clean copy of the manuscript without tracked changes are also uploaded. We also made additional edits to ensure that the manuscript meets PLOS ONE’s style requirements. In this response letter, we have included each of the referees’ comments followed by our reply (Response). Reviewer 1: Comment: The authors applied a two-stage hypothesis testing procedure for testing differentially expressed or spliced isoforms, within a linear mixed model setting. The novelty of the methodology part is limited, as this work can be regarded as an extended application to RNA-seq data, compared to Heller (2009) and Li (2014) where the two-step testing procedure was originally proposed. Nevertheless, the data experiments of this paper were solid and complete, allowing this paper to be a good application report with interesting scientific findings. Response: We appreciate the reviewer’s comment and have added a brief discussion/clarification of this study. Please see the edits on page 5 in the revised manuscript with track changes document. “We present a novel application of two-step hierarchical hypothesis testing procedure coupled with a linear mixed model setting to analyze the differential expression at the multi-dimensional gene- and isoform- level using data from RNA sequencing technologies. ” Minor comment: It is appreciative that the authors provided code for this work. A gentle advice would be providing a demonstration of applying the R functions (i.e. pipeline), rather than the functions only. Response: We have prepared a tutorial in the html format generated through the use of R markdown file illustrating the analytical pipeline. The tutorial that describe the step-by-step instructions on using the collection of R functions is available through http://www.unm.edu/~kanghn/software/Tutorial.html and the Dryad Digital Repository (doi:10.5061/dryad.66t1g1k0h). We have also provided two documents in html format to explain the detailed steps and R codes for analyses of the two example data. Please see edits in the section of Availability of data and materials on page 27 in the revised manuscript with track changes document. Reviewer 2: The authors propose to use a linear mixed model to simultaneously assess genes with differentially expressed or spliced isoforms. A two-step hypothesis testing procedure is proposed to select the significant genes and isoforms, which controls the gene-level overall false discovery rate. The proposed method is novel and enjoys some advantages over the existing methods. The authors further demonstrate the performance of the proposed method using two real data sets and a thorough simulation study. Comment: I only have one comment: the validation of the proposed method relies on the validity of the linear mixed model (1), where the covariance matrix of y has equal off-diagonal elements in (2). Some comments are needed for the validation of such a model. If the assumption of the covariance matrix is violated in applications, the model (1) is not applicable. Is the proposed method robust to use in such a scenario? What modification is need? Response: We appreciate the reviewer’s comment and agree that this is a good point. We provided analyses scripts that incorporating three different choices of covariance matrix (compound symmetry with equal variances, unequal variances, and unstructured), and compared the results in the applications and added discussions on the choices of covariance matrix. If the assumption that the covariance matrix of y has equal off-diagonal is violated, we suggest using the unstructured covariance structure that has no constraints on the parameters resulting in the best model fit. However, we also note some limitations of using the unstructured covariance structure. It will suffer from inflated type I error for relatively small samples as previously reported by Kwok et al. 2007. In the case of large sample sizes, the computation time for incorporating an unstructured covariance structure is much larger than other choices of covariance structures. The compound symmetry with equal variances covariance structure, on the other hand, is limited in capturing the different variances in the different isoform expressions. The unequal variances covariance structure resides in the middle of these two covariance structures. It can be used more broadly because not only does it allow for heterogeneous variances among the isoforms, it also captures the random interaction effects between the subjects and isoforms. It is also a parsimonious model as it has a fewer number of parameters than the unstructured covariance does. We focused on the discussion of our results using the covariance structure with unequal variances from the practical perspective, which accounts for both the computation time and the appropriate covariance structure assumption that suits the majority of genes. Please see edits in the results section (page 12, 17) and discussion section (pages 25-26) in the revised manuscript with track changes document. Kwok O-m, West SG, Green SB. The Impact of Misspecifying the Within-Subject Covariance Structure in Multiwave Longitudinal Multilevel Models: A Monte Carlo Study. MULTIVARIATE BEHAVIORAL RESEARCH. 2007;42(3):557-92. Additional Comments on Journal Requirements: Comment: 1) We note that your manuscript is not formatted using one of PLOS ONE’s accepted file types. Please reattach your manuscript as one of the following file types: .doc, .docx, .rtf, or .tex (accompanied by a .pdf). If your submission was prepared in LaTex, please submit your manuscript file in PDF format and attach your .tex file as “other.” Response: We have reattached the manuscript files as the .docx file type. Comment: 2) Unfortunately, the repository you have noted in your Data Availability statement does not qualify as an acceptable data repository according to PLOS ONE's standards, as it is public but does not appear to be stable. At this time, please upload the minimal data set necessary to replicate your study's findings to a stable, public repository (such as figshare or Dryad) and provide us with the relevant URLs, DOIs, or accession numbers that may be used to access these data. Response: We have uploaded the datasets and analysis scripts for replicating our study findings to the Dryad Digital Repository (doi:10.5061/dryad.66t1g1k0h). The datasets are currently for private access during this review period, which can be accessed through: https://datadryad.org/stash/share/yRDf1Kmj9_hR_I-IGg_vukBVNUmmB9tm_j8v1BZ721A. We have also revised the Data Availability statement to include the Dryad doi. Sincerely, Li Luo, Ph.D. and Huining Kang, Ph.D. University of New Mexico Comprehensive Cancer Center Division of Epidemiology, Biostatistics, and Preventive Medicine Department of Internal Medicine, University of New Mexico Emails: LLuo@salud.unm.edu and HuKang@salud.unm.edu Attachments Attachment Submitted filename: Response to reviewers.pdf https://doi.org/10.1371/journal.pone.0232646.r002
25 Sep 2020 Decision Letter - Zhaohui Qin, Editor Two-Step Mixed Model Approach to Analyzing Differential Alternative RNA Splicing PONE-D-20-11155R1 Dear Dr. Kang, We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements. Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication. An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org. If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org. Kind regards, Zhaohui Qin, PhD Academic Editor PLOS ONE Additional Editor Comments (optional): Reviewers' comments: Reviewer's Responses to Questions Comments to the Author 1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation. Reviewer #1: All comments have been addressed Reviewer #2: All comments have been addressed ******** 2. Is the manuscript technically sound, and do the data support the conclusions? The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented. Reviewer #1: Yes Reviewer #2: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes ****** 4. Have the authors made all data underlying the findings in their manuscript fully available? The PLOS Data policy requires authors to make all data 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 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—e.g. participant privacy or use of data from a third party—those must be specified. Reviewer #1: Yes Reviewer #2: Yes ****** 5. Is the manuscript presented in an intelligible fashion and written in standard English? PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here. Reviewer #1: Yes Reviewer #2: Yes ****** 6. Review Comments to the Author Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters) Reviewer #1: The authors have addressed my comments in the first round of review, particularly, they prepared a much more user-friendly tutorial regarding code usage. I don't have further comments for this submission. Reviewer #2: (No Response) ****** 7. 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 https://doi.org/10.1371/journal.pone.0232646.r003
Formally Accepted
29 Sep 2020 Acceptance Letter - Zhaohui Qin, Editor PONE-D-20-11155R1 Two-Step Mixed Model Approach to Analyzing Differential Alternative RNA Splicing Dear Dr. Kang: I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department. If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org. If we can help with anything else, please email us at plosone@plos.org. Thank you for submitting your work to PLOS ONE and supporting open access. Kind regards, PLOS ONE Editorial Office Staff on behalf of Associate Professor Zhaohui Qin Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0232646.r004

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