Automatic detection of cell-cycle stages using recurrent neural networks

Abin Jose; Rijo Roy; Daniel Moreno-Andrés; Johannes Stegmaier

doi:10.1371/journal.pone.0297356

Peer Review History

Original SubmissionAugust 25, 2023
13 Sep 2023 Decision Letter - Xiao Luo, Editor PONE-D-23-27382Automatic Detection of Cell-cycle Stages using Recurrent Neural NetworksPLOS ONE Dear Dr. Jose, 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. Please submit your revised manuscript by Oct 28 2023 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. 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Note that it is not acceptable for the authors to be the sole named individuals responsible for ensuring data access. We will update your Data Availability statement to reflect the information you provide in your cover letter. 4. We noted in your submission details that a portion of your manuscript may have been presented or published elsewhere. "Yes, Fig. 1. was published in ICASSP 2023.This is a pre-work of this Journal paper. The Journal paper covers advanced model architecture and more experiments." Please clarify whether this [publication] was peer-reviewed and formally published. If this work was previously peer-reviewed and published, in the cover letter please provide the reason that this work does not constitute dual publication and should be included in the current manuscript. [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 Reviewer #3: Yes ******** 2. Has the statistical analysis been performed appropriately and rigorously? Reviewer #1: Yes Reviewer #2: Yes Reviewer #3: N/A ****** 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 Reviewer #3: No ****** 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 Reviewer #3: 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 propose a novel network architecture, called Time Encoded ResNet18 Model, and conduct a series of experiments. Here, i have some questions that need to be answered. 1. Did the authors use any statistical methods at any points in the manuscript? Please updated and include the details in the respective statistical analysis section. 2. Whether the change of hyperparameters will have a drastic effect on the model's performance is hoped that the authors can prove it experimentally. 3.From the performance of label matrix, the superiority of the model proposed by authors seems not obvious, and it is hoped that this results can be further explained. 4. The baseline proposed by authors is relatively few, and it is hoped that some baseline other than RNN-based can be added. Reviewer #2: Jose et al. proposed a novel deep-learning model named Time Encoded ResNet18 to distinguish different mitotic stage. They evaluated the model by comparing it with the base model and other algorithms and proved the overall superior performance. In real datasets, Time Encoded ResNet18 achieved high accuracy and precision. I have some concerns listed below. Major: 1. The accuracies, precisions, recalls, and F-scores showed in Table 3, 5, 7, 9, and 11 did not prove the consistent superior performance of Time Encoded ResNet18. In Table 11, the model reached the top F-score in only 2/6 states, which showed that in some cases, the model could not even outperform the base model. Since the evaluation criteria were quite close among four methods (close to 1.000), I didn’t see the advantage of Time Encoded ResNet18. 2. Fig13 shows the predictions of each method and the true labels. But I can hardly tell which method was better. Authors may compare the key difference of each method and plot the figures focusing on the difference. Minor: 3. Please define the term “sequence” clearly. 4. Please explain why models treat each image independently in line 423. 5. The typo in equation 8. Reviewer #3: In the paper, the authors developed a deep-learning framework to predict mitosis stages from time-lapse microscopy images. They adopted an RNN to capture the connections between successive frames. With several in-depth experiments on two public datasets, they demonstrated that their methods performed better than the baseline methods. Besides, they also studied the features extracted by different models and explained why their RNN model had superior performance. Strength: 1. Instead of viewing each frame as an independent sample, the authors use RNN to capture the time dependency between successive frames. 2. Features extracted by their method could clearly distinguish different mitosis stages. 3. The tracking network in their model can automatically track the center-cell and suppress information from other cells, which brings convenience for users. Weakness: 1. In their literature review, the authors mentioned a state-of-the-art methods to identify cell-cycle stages, CellCognition and LiveCellMiner. However, they did not compare the performance of their model with CellCognition and only compared with LiverCellMiner on the "LiveCellMiner Dataset". In my point of view, other competing methods they adopted were more like an ablation study. Therefore, I suggest adding some experiments to compare the performance of their Time Encoded ResNet18 with CellCognition and LiveCellMiner on both the "LiveCellMiner Dataset" and the "Zhong et al.’s Dataset". 2. In their experiments, the training and testing were performed within the same dataset. However, in practice, after getting some new time-lapse microscopy images, it is usually time-consuming to annotate some of them to get a training set. Therefore, I suggest the authors to test the performance of their model when training on one dataset, say, the LSM710 dataset and predicting the cell-cycle stages of another dataset containing the same type of cells (maybe after fine-tuning on a small fraction of data), say, the LSD1 dataset (also capturing the human HeLa cells). Or, maybe they can test their model on one dataset they used but with a smaller train-test ratio, say, 0.5 or even 0.2. I think these settings will make their model more attractive and helpful for users. 3. It seems that the improvements in accuracy, recall and F-score were quite marginal in some experiments, for example, Table 2, Table 8 and Table 10. Also, I noticed that in several settings, their Time Encoded ResNet18 had a lower F-score than LiveCellMiner for the identification of cells belonging to the mitosis stage (Table 3, Table 7, Table 9). Therefore, I was wondering if the improvements in classification brought by their model can help obtain some biological findings. For example, the authors of LiveCellMiner [1] analyzed the NikonXLight dataset and found that downregulation of INO80, SRCAP, EP400 and H2A.Z consistently lengthens early mitotic progression. They also detailedly analyzed the LSD1 dataset and performed some experiments to support their findings. Hence, I suggest the authors carefully examine the results of at least one of the datasets in their experiments and explain or discuss whether any possible new biological findings can be found when applying their Time Encoded ResNet18 model. 4. In the "Conclusions" part, the authors did not discuss the limitations of their model, which are important for users to correctly use their model and avoid possible pitfalls. Minor points: 1. The authors did not provide their codes for this model, making it difficult to try their methods. 2. In Table 8, the accuracy of LiveCellMiner was higher than the mean accuracy of the Time Encoded ResNet18 model. Therefore, LiveCellMiner should be in boldface rather than Time Encoded ResNet18. 3. In Table 10 (the analysis of Zhong et al.’s Dataset), I suggest adding the frame-to-frame accuracy of TC3, since it performed the best in predicting cells belonging to the interphase. 4. In the last line on page 3, the names of the authors of reference [26] were missing (after the words "in 2016"). References: [1] Moreno-Andr´es D, Bhattacharyya A, Scheufen A, Stegmaier J. LiveCellMiner: A new tool to analyze mitotic progression. PloS one. 2022;17(7):e0270923. ****** 6. 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Revision 1
17 Dec 2023 Author Response Response to the editor. 2. We note that the grant information you provided in the ‘Funding Information’ and ‘Financial Disclosure’ sections do not match. When you resubmit, please ensure that you provide the correct grant numbers for the awards you received for your study in the ‘Funding Information’ section. We have updated this section. 3. In your Data Availability statement : Code is now available in the repo : https://github.com/Rijo756/cell-cycle-stages-identification. Dataset will be updated soon. We are working on it now. 4. We noted in your submission details that a portion of your manuscript may have been presented or published elsewhere. "Yes, Fig. 1. was published in ICASSP 2023. This is a pre-work of this Journal paper. The Journal paper covers advanced model architecture and more experiments." Please clarify whether this [publication] was peer-reviewed and formally published. We have mentioned this in the cover letter, that only the Fig. 1 is resued. Response to the reviewers. We thank the reviewers for their critical assessment of our approach. In the following, we address the major concerns raised point by point, and the corresponding revisions. Comments from Reviewer 1 Comment: 1. Did the authors use any statistical methods at any points in the manuscript? Please update and include the details in the respective statistical analysis section. Reply: Thanks for your comment. Kindly note that the reported numbers in the paper are the average performance of 20 repetitions and we have included the standard deviations of each experiment as well in the tables. Please check line number 447. Comment: 2. Whether the change of hyperparameters will have a drastic effect on the model's performance is hoped that the authors can prove it experimentally. Reply: Thanks for the comment. The results reported have already undergone grid search-based hyperparameter tuning. The optimum hyperparameters selected by the grid search are provided in Table 1, and the standard deviation of 20 repetitions is already mentioned in the paper. Please check line 447 and 448 in the manuscript. Comment: 3. From the performance of label matrix, the superiority of the model proposed by authors seems not obvious, and it is hoped that this results can be further explained. Reply: It is true that visually \fnding the differences in the label matrix can be challenging. To address this, we have increased the size of the label matrix plots to make the differences more clear. Please refer to Fig. 6, Fig. 7, Fig. 10, Fig. 11, and Fig. 13 for visual representations. Additionally, we have included quantitative metrics such as precision, recall, and F-score. To further illustrate the advantages of incorporating time information, we have included the PCA plots. Furthermore, the accuracy, which is a qualitative measure of the label matrix, is clearly demonstrated in the accuracy table, highlighting the advantages of the proposed method. We have further added the following sentence in the results section. "The label matrix generated by the ResNet18 model contains some misclassi\fcations in the post-mitosis class and also in the mitosis class for some sequences which is eliminated by the proposed RNN-based model. The same behaviour is observed for the other datasets as well which is shown in the respective subsections." (Please refer to line numbers 552 to line 555 in the manuscript). Comment: 4. The baseline proposed by authors is relatively few, and it is hoped that some baseline other than RNN-based can be added. Reply: Kindly note that, we have included the ResNet18 model which is not an RNN-based approach for comparison. We have also compared our model with LiveCellMiner paper and the baseline model we have developed. We are con\fdent that the proposed baseline re ects the state-of-the-art. Comments from Reviewer 2 1 Comment: 1. The accuracies, precisions, recalls, and F-scores showed in Table 3, 5, 7, 9, and 11 did not prove the consistent superior performance of Time Encoded ResNet18. In Table 11, the model reached the top F-score in only 2/6 states, which showed that in some cases, the model could not even outperform the base model. Since the evaluation criteria were quite close among four methods (close to 1.000), I didn't see the advantage of Time Encoded ResNet18. Reply: Kindly note that, even though the results for the Inter, Prometa, Meta, and Telo phases are somewhat lower, they are quite close to those of the best-performing model. In the two states where the results are better, we observe that the numbers are signi\fcantly higher compared to the second best-performing model. Also please refer to the Tables 3, 7, and 9 in the results section. It is quite clear that the results are only lower for mitosis state and for the other two states, the F-score is already higher. Comment: 2. Fig. 13 shows the predictions of each method and the true labels. But I can hardly tell which method was better. Authors may compare the key difference of each method and plot the \fgures focusing on the difference. Reply: Thanks for pointing out this. It is true that label matrix plots were small. Now we have increased the size of the plots and updated the label matrix plots and the latent space plots. Kindly refer to the response to comment 3 of reviewer 1 in the rebuttal letter. Minor comments: Comment: 3. Please de\fne the term \\sequence" clearly. Reply: Kindly note that a "Sequence" typically refers to a series of related or connected frames. We have indicated that now in the introduction. Please refer to line 13 in the manuscript. Comment: 4. Please explain why models treat each image independently in line 423. Reply: The image is treated independently here as it is a classi\fer model and time information is not used. Please refer to line 422 in the manuscript. Comment: 5. The typo in equation 8. Reply: We have now corrected the typo in equation 8. Please refer to line 418. Comments from Reviewer 3 Comment: 1. In their literature review, the authors mentioned a state-of-the-art methods to identify cell-cycle stages, CellCognition and LiveCellMiner. However, they did not compare the performance of their model with CellCognition and only compared with LiverCellMiner on the "LiveCellMiner Dataset". In my point of view, other competing methods they adopted were more like an ablation study. Therefore, I suggest adding some experiments to compare the performance of their Time Encoded ResNet18 with CellCognition and LiveCellMiner on both the "LiveCellMiner Dataset" and the "Zhong et al.'s Dataset". 2 Reply: CellCognition is an annotation tool used for \fnding complex cellular dynamics. It uses a whole- slide 2D+t image which contains multiple cells to detect objects and classify them into cell-cycle stages. However, in our case, both the datasets are single cell datasets which contain only one cell. Since our focus is on single cell image datasets, it is not reasonable to compare with CellCognition. Furthermore, the Zhong et al.'s dataset even uses CellCognition for object detection and feature extraction from the whole-slide images. Kindly note that, the comparison results of the performance of the Time Encoded ResNet18 with LiveCellMiner and TC3 are already available. Comment: 2. In their experiments, the training and testing were performed within the same dataset. However, in practice, after getting some new time-lapse microscopy images, it is usually time-consuming to annotate some of them to get a training set. Therefore, I suggest the authors to test the performance of their model when training on one dataset, say, the LSM710 dataset and predicting the cell-cycle stages of another dataset containing the same type of cells (maybe after \fne-tuning on a small fraction of data), say, the LSD1 dataset (also capturing the human HeLa cells). Or, maybe they can test their model on one dataset they used but with a smaller train-test ratio, say, 0.5 or even 0.2. I think these settings will make their model more attractive and helpful for users. Reply: Thanks for the comment. We guess the performance will drop quite a bit if we change the modality. The proposed experiment of checking what fraction of ground truth is needed to obtain good results would, however, be quite interesting and also a hint for potential users how many images they should annotate to obtain good results. We have done the experiment for train test ratio of 0.5 and the results are now updated in Table 10. It shows that the numbers do not drop signi\fcantly indicating that even with a smaller dataset, the model is able to give good performance. Comment: 3. It seems that the improvements in accuracy, recall and F-score were quite marginal in some experiments, for example, Table 2, Table 8 and Table 10. Also, I noticed that in several set- tings, their Time Encoded ResNet18 had a lower F-score than LiveCellMiner for the identi\fcation of cells belonging to the mitosis stage (Table 3, Table 7, Table 9). Therefore, I was wondering if the improvements in classi\fcation brought by their model can help obtain some biological \fndings. For example, the authors of LiveCellMiner [1] analyzed the NikonXLight dataset and found that downregulation of INO80, SRCAP, EP400 and H2A.Z consistently lengthens early mitotic progres- sion. They also detailedly analyzed the LSD1 dataset and performed some experiments to support their \fndings. Hence, I suggest the authors carefully examine the results of at least one of the datasets in their experiments and explain or discuss whether any possible new biological \fndings can be found when applying their Time Encoded ResNet18 model. Reply: Thank you for your comment. We were not able to make any new biological conclusions from the experiments. However, in our experiments with the 6-class dataset, we observed that there is a higher rate of misclassi\fcation between adjacent classes in the case of the time-encoded ResNet18 and the misclassi\fcation value is quite low for the other classes. This phenomenon comes as an additional advantage stemming from the inclusion of time information. In contrast, for the ResNet18 model without the incorporation of time information, misclassi\fcations are distributed more evenly across all classes. We have added this information now in the paper. Please refer to line numbers 654-659 in the manuscript. 3 Comment: 4. In the "Conclusions" part, the authors did not discuss the limitations of their model, which are important for users to correctly use their model and avoid possible pitfalls. Reply: We have updated the 'Conclusions' section of the paper and included the limitations of the paper and future work. Please refer to line numbers 690-702. Minor points: Comment: 5. The authors did not provide their codes for this model, making it difficult to try their methods. Reply: The github link (https://github.com/Rijo756/cell-cycle-stages-identi\fcation) is added now. (See the footnote in page 13.). Comment: 6. In Table 8, the accuracy of LiveCellMiner was higher than the mean accuracy of the Time Encoded ResNet18 model. Therefore, LiveCellMiner should be in boldface rather than Time Encoded ResNet18. Reply: Thanks for noting this mistake. We have updated Table 8. Comment: 7. In Table 10 (the analysis of Zhong et al.'s Dataset), I suggest adding the frame-to- frame accuracy of TC3, since it performed the best in predicting cells belonging to the interphase. Reply: We have referred the TC3 [19] paper for the accuracy numbers. The accuracy value is not reported as a number, but the supplementary \fle contains a graph showing how the number of features affect the accuracy and the highest value reported for the TC3 model in the paper is 94:1%. We have added that now in the manuscript. Please refer to Table 11. It is true that the accuracy is slightly lower for the proposed method. Also, please notice that for the pro, prometa, meta, anaphase, and telo-phases the F-score is higher than the TC3 approach. These phases occur much more rarely than the interphase and it is thus great that our method performs better here. Comment: 8. In the last line on page 3, the names of the authors of reference [26] were missing (after the words "in 2016"). Reply: Thanks for noting this typo. We have updated it. Please check line 113. Attachments Attachment Submitted filename: Rebuttal_letter.pdf https://doi.org/10.1371/journal.pone.0297356.r002
4 Jan 2024 Decision Letter - Xiao Luo, Editor Automatic Detection of Cell-cycle Stages using Recurrent Neural Networks PONE-D-23-27382R1 Dear Dr. Abin Jose, 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, Xiao Luo 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 #2: All comments have been addressed Reviewer #3: 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 #2: Yes Reviewer #3: Yes ****** 3. Has the statistical analysis been performed appropriately and rigorously? Reviewer #2: Yes Reviewer #3: N/A ****** 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 #2: Yes Reviewer #3: 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 #2: Yes Reviewer #3: 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 #2: All my concerns were addressed. Nice work! No more comments. All my concerns were addressed. Nice work! No more comments. (Twice for meeting the character count requirement) Reviewer #3: I thank the authors for their detailed responses of all the reviewer comments. I have no further concerns and feel that it is now suitable for being published. ****** 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 #2: No Reviewer #3: No ******** https://doi.org/10.1371/journal.pone.0297356.r003
Formally Accepted
15 Feb 2024 Acceptance Letter - Xiao Luo, Editor PONE-D-23-27382R1 PLOS ONE Dear Dr. Jose, I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team. At this stage, our production department will prepare your paper for publication. This includes ensuring the following: * All references, tables, and figures are properly cited * All relevant supporting information is included in the manuscript submission, * There are no issues that prevent the paper from being properly typeset If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps. Lastly, 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 customercare@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 Dr. Xiao Luo Academic Editor PLOS ONE https://doi.org/10.1371/journal.pone.0297356.r004

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