SinglePointRNA, an user-friendly application implementing single cell RNA-seq analysis software

Laura Puente-Santamaría; Luis del Peso

doi:10.1371/journal.pone.0300567

Peer Review History

Original SubmissionMay 3, 2023
24 Aug 2023 Decision Letter - Andrea Tangherloni, Editor PONE-D-23-13388SinglePointRNA, an user-friendly application implementing single cell RNA-seq analysis softwarePLOS ONE Dear Dr. Puente-Santamaria, 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. The reviewers agreed that the proposed work is interesting and that the shiny app could be relevant for the research community devoted to single-cell RNA seq analysis. However, both reviewers pointed out that a revision is required to improve and strengthen the current version of the manuscript. In particular, a better literature review is required to show the difference between the proposed method and the state-of-the-art approaches/apps (e.g., SCHNAPPs and ShinyCell). Provide the readers with more details regarding the architectural implementation of the application. Please add some examples of downstream analysis to show the convenience of the proposed method. Please describe the scRNA-seq analysis performed using the proposed application (e.g., the test used for DEGs). The strengths, limitations, and possible future extensions of the proposed app must be better discussed. One of the reviewers also reported several suggestions to improve the quality of the shiny app; please take them into consideration. Finally, all the Reviewers highlighted that spell-checking and proofreading of the manuscript are strongly required. Please refer to the reviewers' reports for detailed comments that could be helpful to you in improving the current version of the manuscript. Please submit your revised manuscript by Oct 08 2023 11:59PM. 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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: N/A 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: No 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: In this paper, the authors present a Shiny-based application (SinglePointRNA) that provides a graphical operator interface for scRNA-seq analysis tools. It avoids extensive coding efforts and is useful for a wide range of users from novice to expert. However, there are still some problems in the manuscript. The following are comments on this work. Major comments: 1. The author should detail the method implementation strategy, such as the architectural implementation of the application. 2. Because one of the greatest contributions of this method is more convenient data analysis, the authors should add examples of downstream analysis to illustrate the convenience of the method. 3. The authors should systematically describe what scRNA-seq analysis the application can perform. 4. The authors should give the temporal performance of the application. Minor comments: 1.The format of this paper should be improved. 2.The authors should proofread the English writing to improve the study. Reviewer #2: In my opinion, the work is very interesting and the tool could be relevant for the research community devoted to single-cell rna seq analysis. The installation of the software is simple. The documentation on the github page is very clear. I tested the shiny app also on another sc-dataset (GSE110949) to understand possible limits/problems of the shiny app. The paper could be accepted after some revisions. -------------------------------------------------------------------------------------- 1. For the manuscript Some other apps for single-cell RNAseq analysis are already available. For example, Jagla, B., Libri, V., Chica, C., Rouilly, V., Mella, S., Puceat, M., & Hasan, M. (2021). SCHNAPPs-single cell sHiNy APPlication (s). Journal of Immunological Methods, 499, 113176. Ouyang, J. F., Kamaraj, U. S., Cao, E. Y., & Rackham, O. J. (2021). ShinyCell: simple and sharable visualization of single-cell gene expression data. Bioinformatics, 37(19), 3374-3376. What are the main differences/improvements between singlePointRNA app and other apps for single-cell RNAseq analysis? Discuss these differences and the possible state of the art for this task. Which type of statistical test is used for DEGS? Are there any future work planned? Please add this to the conclusions if any. Check for some typos. For example, Caption of Figure 1. “in this section are the widget..”. Improve the resolution of the figures. ------------------------------------------------------------------------- 2. The shiny app I tested the various sections of the app. I have some suggestions/comments. A. Load dataset. This menù is very clear and I loaded the dataset without any problem. However I also suggest a pop-up during the loading phase. Moreover, it could be useful to specify how genes/cells must be organized in the csv file, i.e. genes on the rows and cells on the columns. Finally, a button to clear the actual dataset could be useful before loading another dataset B. QC analysis and filter cells. In the table related to the default filters there is “-INF” for the minimum value of “Maximum mitocondrial RNA (%)” and “Maximum ribosomal RNA (%)”, but the minimum value should be 0. Typo “SCTrasnform” in the menu normalization. There is no option/menù to exclude genes/features, i.e. mitochondrial genes or genes not detected in a minimum number of cells, which is very common in the sc-analysis. I think it could be useful to add this option. Is it possible to return to the original dataset and re-apply the filters? Typo ““Numeric fiters” “nomalizing” (pop up menù of filter cells) C. Cluster Why the number of neighbors is not considered as parameter? D. 2D visualization. This section is very interesting with the possibility to test several seeds for tsne and umap. However, it is necessary to fix graphical interface with the six plots because some plots is out of the gui. Moreover, for a comparison maybe it is better to use the same scale range for the horizontal and vertical axis. E. Differential expression. Which statistical test is used for comparison? It could be useful to add colorbar in the heatmap F. Dataset plots Maybe it could be useful to add also the points in the violin plots of the “Plot gene expression” . G. Altered pathways Very clear and well organized. Just use the name of the groups in the legend of altered pathways instead of “A” and “B” H. User’s guide. Very clear and well organized.Just a comment. In "What's under the hood? Classification section". Usually, machine learning algorithms are divided between supervised and unsupervised learning (not classification). Indeed, classification is usually referred to a specific supervised learning in which the output is categorical. Correct this fact in this section. ****** 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". 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Revision 1
23 Oct 2023 Author Response Dear Dr. Andrea Tangherloni, Thank you for your valuable feedback on our manuscript (PONE-D-23-13388) entitled "SinglePoint RNA, an user-friendly application implementing single cell RNA-seq analysis software" by Laura Puente and Luis del Peso. Below, we provide a detailed response addressing the concerns raised by the referees, along with explanations of the modifications made to the manuscript text and figures. On accordance with PLoS One format guidelines, we have made the following changes: 1. We have converted the figure file format to TIFF. 2. Author affiliation section has been revised. 3. The Data Availability statement has been expanded to specify in greater detail that the code and data used in this project is fully available through a GitHub repository at www.github.com/ScienceParkMadrid/SinglePointRNA With regards to copyrighted images, we are submitting the signed permission form to display a screenshot of the software in Figure 1. Figure 5 has been changed and no longer displays an excerpt of the application's materials. Though I want to note that the image displayed in Figure 1 is not a reprint of another publication, but an edited screenshot taken in a personal computer after executing the code, which is distributed under a license that allows even patent and commercial use to third parties. Considering this, the wording added to the caption (“Reprinted from [ref] under a CC BY license, with permission from [name of publisher], original copyright [original copyright year].”) doesn't really match the context, so I'd like to ask if there was a better fitting formula to add to the figure's caption. In response to the specific queries raised by Reviewer 1: Major comments: 1 - We have replaced figure 5 for an overview of the app's structure. 2- We appreciate the referee's recognition of one of the key advantages of SinglePointRNA, which is its ability to simplify scRNAseq data analysis. We acknowledge the importance of providing practical examples to illustrate the convenience and utility of our method. To maintain the conciseness of the manuscript and to ensure that readers have easy access to these illustrative examples, we have chosen to include them in the User's Guide associated with the application. The User's Guide serves as a comprehensive resource for users of SinglePointRNA, providing step-by-step instructions and examples for each analysis included in the package. By placing these examples in the guide rather than in the manuscript, we aim to offer users a more user-friendly and practical experience when working with the tool. This approach allows readers to access detailed guidance precisely when they need it, making it easier for them to apply SinglePointRNA effectively to their scRNAseq data analysis. We believe that this strategy strikes the right balance between providing valuable examples and maintaining the manuscript's clarity and brevity. Users can conveniently access the User's Guide alongside the application to explore and understand the practical applications of SinglePointRNA. 3- We've added the list of analysis steps implemented in the Introduction (lines 25-28 of the manuscript) 4- SinglePointRNA is designed with a primary focus on enhancing user-friendliness and accessibility for inexperienced users, aiming to simplify the scRNAseq analysis process. It is important to clarify that the application does not aim to improve the speed or performance of the underlying tools it integrates. We intentionally exclude certain options, such as parallelization, to prevent significant increases in memory requirements, which could pose challenges for users with limited computational resources. In summary, SinglePointRNA prioritizes ease of use and accessibility for its target audience, and as such, its emphasis is on simplifying analysis steps rather than on optimizing performance speed. Minor comments: We have reviewed and revised the format and writing of the manuscript to improve clarity and readability. Answer to queries from Reviewer 2 Comments on the manuscript We greatly appreciate the referees' comments and their valuable insights into the comparison of SinglePointRNA, our Shiny-based R application, with existing tools such as ShinyCell, SCHNAPPs, and SEQUIN. We understand the importance of assessing SinglePointRNA's uniqueness and its contributions to the field of scRNAseq analysis. ShinyCell (Bioinformatics, 37(19), 2021, 3374–3376) and similar tools, like scViewer (Cells. 2023 May 27;12(11):1489), primarily focus on visualizing processed single-cell data. They require preprocessed data as input and concentrate on visualization, making them complementary rather than direct alternatives to our app. SinglePointRNA, on the other hand, emphasizes comprehensive scRNAseq data processing, interactive visualization, and functional insights, thereby providing a broader utility. Regarding SCHNAPPs (Journal of Immunological Methods 499 (2021) 113176), while it encompasses essential functions for complete scRNAseq data processing, it lacks certain features that enhance biological interpretability. Specifically, SinglePointRNA introduces functional enrichment analysis of differentially expressed genes and automatic cell type imputation, both aimed at enhancing the biological relevance of the results. The incorporation of two novel metrics, co-clustering conservation and clustering uncertainty score, in conjunction with interactive Sankey flowcharts represents a key advancement, helping users make informed decisions on clustering resolution, a critical parameter for robust clustering outcomes. We consider these unique characteristics as defining improvements that set SinglePointRNA apart from existing tools. Moreover, we acknowledge the recent publication of SEQUIN (2023, Cell Reports Methods 3, 100420) around the time of our manuscript submission to PLoS ONE. SEQUIN as SinglePointRNA was designed for non-bioinformaticians for complete raw scRNAseq analysis. Both tools provide dedicated graphs for parameter selection, and functional enrichment analysis of differentially expressed genes. However, an important distinction lies in the deployment approach: SinglePointRNA operates primarily as a stand-alone tool, offering local installation and operation flexibility. In contrast, SEQUIN leans towards web-based functionality, which may impose limitations on the scale of analyses. Furthermore, SinglePoint's novel metrics extend beyond providing results for a range of clustering resolution values, offering deeper insights into clustering performance. In conclusion, we believe that SinglePointRNA, through its comprehensive processing capabilities, unique metrics, and flexibility in deployment, represents a significant advancement over existing tools like SCHNAPPs and SEQUIN. Its focus on facilitating sound clustering decisions and enriched biological interpretations positions it as an invaluable asset for the scRNAseq analysis community. To acknowledge the existence of other Shiny-based scRNAseq tools we have included the following sentence in the discussion of our manuscript. "We acknowledge the existence of other Shiny-based scRNAseq tools such as SCHNAPPs and SEQUIN. SCHNAPPs (Journal of Immunological Methods 499, 2021) presents comprehensive processing and analysis capabilities for raw scRNAseq data. However, SinglePointRNA, extends its utility by incorporating functional enrichment analysis and automatic cell type imputation to enhance biological insights. Notably, SinglePointRNA introduces novel metrics, co-clustering conservation and clustering uncertainty score, aiding users in selecting a meaningful clustering resolution, a critical parameter to get meaningful results. Furthermore, SEQUIN (Cell Reports Methods 3, 2023), a web-based tool, surfaced around the time of our manuscript submission. While SEQUIN shares many features with SinglePointRNA, such as parameter guidance and functional enrichment analysis, SinglePointRNA is uniquely positioned as a stand-alone tool with flexibility in local deployment. We appreciate these tools' contributions, yet SinglePointRNA's distinctive attributes underscore its value as an insightful alternative for scRNAseq analysis." With regards to differential expression testing, as indicated in the manuscript (page 6 lines 147-148) SinglePointRNA uses the default DE test implemented in Seurat, which is based on the Wilcoxon Rank Sum test. We made this choice to take advantage of the flexibility of the test, because the input for our app is not restricted to particular protocols. Several of the tests available on Seurat are restricted to certain types of scRNA-seq data, such as UMI-tagged datasets for the methods "negbinom" and "poisson", and the more known methods in bulk RNA-seq, like DESeq2, which novice users could be more familiar with, are not as robust with the sample sizes typically compared in single-cell analysis. As for future work planned, this project was developed as part of a pre-doctoral project that has already finished, so there are no plans established to keep expanding it, as long as I am concerned. However, this software was developed initially for the Genomis Unit at the Fundación Parque Científico de Madrid, which might have an interest in furthering development of the software, and in addition to that, it was published under an open source license that allows any member of the community to distribute their own modifications of the app, which we welcome with open arms. All figures are now over 2000px wide. Comments on the app. 1. The pop-up notification for the loading tab has been added. Clearing the data can be done by refreshing the app or the browser window, but for convenience we've added reset buttons on the loading and dataset merging tabs. 2. Regarding the issue of '-Inf' in the QC section, while we were unable to reproduce it, we have implemented controls to address it if it occurs. Regarding the request to exclude genes or features, such as mitochondrial genes or genes not detected in a minimum number of cells, which is a common requirement in single-cell analysis, we have a solution. In the 'Filter cells' tab, you will find the second-to-last option, which is designed to regress the effect of a variable on the dataset. This feature includes the expression levels of mitochondrial, ribosomal, and NRY genes, as well as genes used to estimate cell cycle phases. Regarding the ability to return to the original dataset and reapply the filters, please note that this would nearly double the memory requirements of the app to store a copy of the dataset. To provide users with insights into the filtering process, we've implemented a 'Check Filters' button. This feature allows users to assess how many cells might not pass the filter criteria before executing the task. We hope these features enhance the usability and efficiency of SinglePointRNA for your single-cell analysis needs. 3. On the clustering section, there isn't any specific reason to exclude number of neighbors, but since it isn't a direct parameter of the clustering algorithm, like clustering resolution, and it doesn't affect other steps like the number of principal components used, we just did not focus on it. 4. The main point of of the visualization section is to show how much 2D plots can differ (so novice users don't take them as direct measures of similitude) and to choose a convenient one to use as a base to plot results onto. Otherwise, the X-Y variables in each plot don't have to be directly comparable, much less by visual inspection alone. 5. For differential expression, as stated previously, the tests used is the Wilcoxon Rank Sum Test implemented in Seurat. Adding a color bar in heatmaps might be useful in very small datasets, but the function from Seurat that we're implementing in this section clips high expression values and sample sizes are usually too large to differentiate between color shades. As it happens with the heatmaps, large sample sizes make adding a point layer to violin plots an issue. In this case, the dots tend to obscure the value distribution almost completely unless the opacity and dot size are turn to values so low that the whole dot layer becomes useless. 6. On the Altered Pathways section, group names have been added to legends. 7. The introduction to classification on the documentation has been revised. Final statement We extend our sincere gratitude to the reviewers for their meticulous evaluation of our package and manuscript, and for their valuable suggestions, which have significantly enhanced both SinglePointRNA and the manuscript. We believe that the manuscript is now prepared for publication in PLOS ONE. Attachments Attachment Submitted filename: Response to Reviewers.pdf https://doi.org/10.1371/journal.pone.0300567.r002
4 Dec 2023 Decision Letter - Andrea Tangherloni, Editor PONE-D-23-13388R1SinglePointRNA, an user-friendly application implementing single cell RNA-seq analysis softwarePLOS ONE Dear Dr. Puente-Santamaria, 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. The authors addressed the main Reviewers’ comments; however, there are still two main concerns that must be carefully considered: 1) the quality and resolution of the figures must be improved, and 2) a better comparison with the state-of-the-art apps for scRNAseq analysis is required. The authors must report the details of this comparison in the manuscript. They could create a specific subsection related to the comparison between the proposed tool and other tools, including ShinyCell. Please refer to the reviewers' reports for detailed comments that could be helpful to you in improving the current version of the manuscript. Please submit your revised manuscript by Jan 18 2024 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 plosone@plos.org. When you're 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. Please include the following items when submitting your revised manuscript: A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'. 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, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter. If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols. We look forward to receiving your revised manuscript. Kind regards, Andrea Tangherloni Academic Editor PLOS ONE Journal Requirements: Please 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. Additional Editor Comments : The authors addressed the main Reviewers’ comments; however, there are still two main concerns that must be carefully considered: 1) the quality and resolution of the figures must be improved, and 2) a better comparison with the state-of-the-art apps for scRNAseq analysis is required. The authors must report the details of this comparison in the manuscript. They could create a specific subsection related to the comparison between the proposed tool and other tools, including ShinyCell. Please refer to the reviewers' reports for detailed comments that could be helpful to you in improving the current version of the manuscript. [Note: HTML markup is below. Please do not edit.] 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: (No Response) Reviewer #2: (No Response) ******** 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: All my previous comments have been addressed. However, all the figuresare very blurry. Please improve the resolution of all figures. Reviewer #2: The authors fixes my main concerns about this work and the associated Shiny app. My last concern remains on the comparison with the state-of-the-art apps for scRNAseq analysis. In my opinion, the authors should report the details about this comparison, reported on the reviewer's answer, on the manuscript also. My suggestion is to create a specific sub-section related to the comparison between their tool and other tools (and including also ShinyCell). ****** 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 ******** [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.] 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 PLOS at figures@plos.org. Please note that Supporting Information files do not need this step. https://doi.org/10.1371/journal.pone.0300567.r003
Revision 2
16 Jan 2024 Author Response Dear Dr. Andrea Tangherloni, Thank you again for the feedback on our manuscript (PONE-D-23-13388) entitled "SinglePoint RNA, an user-friendly application implementing single cell RNA-seq analysis software" by Laura Puente and Luis del Peso. Below, we provide a detailed response addressing the comments by the referees, along with explanations of the modifications made to the manuscript text and figures. In response to the specific queries raised by Reviewer 1: We have noticed that the figures are shown in a much lower resolution in the manuscript PDF file. The full sized images should be accessible through the link at the top right corner of each page. Nevertheless, we have increased both the resolution and size of all the figures. In response to the specific queries raised by Reviewer 2: The comparison between SinglePointRNA and other scRNA-seq related software has been expanded into its own section: "The growing prominence of single-cell RNA sequencing (scRNAseq) has spurred the development of various tools dedicated to simplifying data visualization and analysis. Notable among these are ShinyCell, scViewer, and SCHNAPPs. Here, we discuss the distinctive features of SinglePointRNA in comparison to these tools, emphasizing its contributions to the state of the art in scRNAseq analysis. ShinyCell, along with counterparts like scViewer, primarily focuses on sharing and visualizing preprocessed single-cell data. These tools serve as valuable complements to SinglePointRNA, as they demand preprocessed data as input and emphasize data visualization. In contrast, SinglePointRNA offers a more comprehensive approach, placing a strong emphasis on scRNAseq data processing, interactive visualization, and functional insights. This broader utility positions SinglePointRNA as a versatile tool for researchers seeking a comprehensive solution. While SCHNAPPs encompasses essential functions for complete scRNAseq data processing, it lacks certain features that enhance biological interpretability. In comparison, SinglePointRNA introduces functionalities such as functional enrichment analysis of differentially expressed genes and automatic cell type imputation. In addition the incorporation of two novel metrics, co-clustering conservation and clustering uncertainty score, along with interactive Sankey flowcharts, represents a significant advancement. These features aid users in making informed decisions on clustering resolution, a crucial parameter for robust clustering outcomes. These unique characteristics set SinglePointRNA apart, adding depth and biological relevance to scRNAseq analysis. During the manuscript submission, an additional tool named SEQUIN, designed for non-bioinformaticians conducting complete raw scRNAseq analysis and sharing similarities with SinglePointRNA, was introduced. Both tools facilitate parameter selection and functional enrichment analysis of differentially expressed genes through dedicated graphs. However, a crucial distinction arises in their deployment approaches. SinglePointRNA operates primarily as a stand-alone tool, allowing for local installation and operational flexibility. In contrast, SEQUIN leans towards web-based functionality, potentially imposing limitations on the scale of analyses. Additionally, SinglePointRNA's novel metrics offer deeper insights into clustering performance, providing researchers with a comprehensive toolkit for robust analysis. Thus, SinglePointRNA stands out as a significant advancement in scRNAseq analysis, showcasing comprehensive processing capabilities, unique metrics, and flexible deployment options. Notably, its emphasis on empowering researchers with informed clustering decisions and enriched biological interpretations positions SinglePointRNA as an invaluable asset for the scRNAseq analysis community. The incorporation of novel metrics and a user-centric approach reflects its commitment to providing a robust toolkit for researchers seeking deeper insights into their single-cell RNA sequencing data." Final statement We want again to express our appreciation to the reviewers for their time and dedication in assessing our work. Your input has undoubtedly contributed to the overall enhancement of our manuscript, and we are grateful for your expertise and commitment to the peer review process. Yours sincerely, Laura Puente Santamaría Attachments Attachment Submitted filename: Response to Reviewers.pdf https://doi.org/10.1371/journal.pone.0300567.r004
1 Mar 2024 Decision Letter - Andrea Tangherloni, Editor SinglePointRNA, an user-friendly application implementing single cell RNA-seq analysis software PONE-D-23-13388R2 Dear Dr. Puente-Santamaria, 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, Andrea Tangherloni 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: N/A 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: (No Response) 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.0300567.r005

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