PONE-D-21-17376

Graphlet eigencentralities capture novel central roles of genes in pathways

PLOS ONE

Dear Dr. windels,

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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

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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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

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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

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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 paper "Graphlet eigencentralities capture novel central roles of genes in pathways." shows an interesting methodology for analyzing the relevance of nodes in complex biochemical networks. It shows how to take advantage in a deeper way of the information in the graphlet adjacency matrix to reveal the most important nodes in a network in terms of its biological functions. In particular, the paper focuses its attention on analyzing networked data coming from biochemistry experiments, e.g., protein-protein interaction networks, metabolic networks, and genomic networks.

The main idea is to use all topological information in the graphlets, infer relevance and biological function based on how nodes participate in some graphlets. In addition, the authors adopt the heuristics of eigencentrality. In few words, it establishes that a node is important if the neighbors of such node are also important.

The information that graphlet adjacency matrix offers can enrich the 'omics' networked data and give insights into the function of the nodes in a biomolecular network.

This approach is related to the one developed in [1], where the networks' information is enriched using similarities between neighborhoods through various metrics. In this sense, I recommend mentioning the analogies of both approaches when exploiting topological information more deeply. Moreover, both use the heuristic of self-centrality as a methodology for calculating the relevance of a node. In addition, I recommend to add some reviews about centrality in complex networks such as [2] or [3] to complete the literature on the topic of centrality measures. In addition, eigencentrality is due to [4] but instead is cited a general book that loses the author's original inspiration. I recommend citing the original author of the method.

Although the concept of centrality itself has not yielded universal results for all types of networks, it is because centralities are typically inspired in some definition of importance that can not be applied to all kinds of networks. In this sense, eigencentrality based on graphlets seems to be a good way to capture different types or notions of centrality behind nodes.

In general, the paper is quite clear and interesting, the methodology is mathematically correct, and its applications are highly relevant for biochemists and sociologists, physicists, computer scientists, among many other disciplines, since the methodology is universal.

Before adding the complementary references, I think that the work could be published.

[1] Alvarez-Socorro, A. J., Herrera-Almarza, G. C., & González-Díaz, L. A. (2015). Eigencentrality based on dissimilarity measures reveals central nodes in complex networks. Scientific reports, 5(1), 1-10.

[2] Xiaolong, R., & Linyuan, L. (2014). Review of ranking nodes in complex networks. Chinese Science Bulletin, 59(13), 1175-1197.

[3] Landherr, A., Friedl, B., & Heidemann, J. (2010). A critical review of centrality measures in social networks. Business & Information Systems Engineering, 2(6), 371-385.

[4] Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. Journal of mathematical sociology, 2(1), 113-120.

Reviewer #2: Overview:

This manuscript builds up on previous work published by the authors regarding graphlets and node adjacency in biological networks. Here, they introduce the concept of graphlet-based eigencentralities to identify pathways and cancer mechanisms described by different graphlet adjacencies. Interestingly, through graphlet eigencentralities the authors show that specific graphlet adjacencies describe functionally similar biological processes and also predict central cancer driver genes. There is a load of both sophisticated and complicated work behind the manuscript and the provided Supplement is crucial to allow the reader to better understand and follow the concepts. It would be very interesting if graphlet theory as described in this manuscript could predict implicated genes in other diseases as well, especially rare ones. Also, since the described theory is complicated, a future direction for the authors would be to create a software suite that automates the described pipeline to extract knowledge.

Major Comments:

1. Supplement (page 11): “different graphlet eigencentralities are positively correlated with each other and most existing centrality measures”. I was wondering what results the simple degree metric (instead of the sophisticated graphlet eigencentralities) would yield, regarding the cancer central gene case studies. Have the authors tried to compare these? For example, what is the degree of HMGA2 in the underlying network regarding the FSAHF case study? In a nutshell: how do graphlet eigencentralities outperform simple topological metrics in the topic of uncovering key implicated players in perturbed pathways?

2. Could the detection of key genes through graphlet eigencentralities work on other diseases? Or is there something special regarding cancer pathways (such as the cross-talk among them) that allows the global pathway centralities to predict these key genes only in this scenario? The authors could include a third case study with a non-cancer disease pathway to figure this out.

Minor Comments:

1. The introduction has been structured sufficiently, explaining all concepts relative to the manuscript and leading the reader to the problem/objective which is presented. Regarding the three approaches for inferring gene functions in networks; I am not sure Centrality (and other topological metrics such as clustering coefficient and degree) is distinguishable from Topology. Maybe rename the third category specifically into “graphlet-based”, since this has already been established by the authors previous publications.

2. Figure 1: Need to sharpen resolution.

3. Figure 1-C: Refer to it in text after (3) is described.

4. “(Section )” is written a number of times across the manuscript. Is this supposed to be a link? Else, write down the respective sections.

5. Explain λ (line 131) a bit more; λ are eigenvalues for which a non-zero eigenvector solution exists.

6. Maybe use another letter to describe the extended definition of adjacency instead of AGi, which is used for the standard graphlet adjacency matrix to avoid confusion. As I understand (line 148), cGiuv is the value of the standard graphlet adjacency AGi(u,v), correct?

7. Is there a brief explanation on why the degree is raised to the power of -1/2 in (5)? Could 1/degree also work here?

8. (line 212) comma typo: “that ,like” -> “that, like”

9. (lines 214-215) What is the difference between “20% of the genes of each pathway” and “20% of the pathway annotated genes”? Maybe rephrase to better clarify.

10. (line 219) “For each pathway”: Write down which pathways were used here.

11. Supplement (Page 44): “Here validate”: typo: “Here we validate”

12. Number the sections in the main manuscript as referred to in the Supplement (e.g., “see Section 2.7.1 in the main paper”), or write the section names 9instead of numbers) in the Supplement.

13. (line 316) Typo: “for the purpose pathway participation” -> “for the purpose of pathway participation”

14. (line 317-319): “In Supplementary Figure 17-A we observe that regardless of the underlying graphlet adjacency, our local approaches and GeneMANIA consistently perform better than random”. Add in parentheses “red line” or “AUC-ROC = 0.5”, to better clarify.

15. (line 339) Typo: fonund -> found

16. (line 409) Typo: enables -> enable

17. (line 431) explain what “MWU” stands for (Mann-Whitney U test). This is written in the Supplement but the main manuscript should be complete by itself.

18. Figure 5: color coding stated on the figure legend is not correct. All nodes appear red instead of red/yellow “nodes represent non-cancer-related genes (red) and known cancer driver genes RB1 and TP53 (yellow)”. Also, node ASF1A on the left figure should be moved, to not obscure the TP53-RB1 connection.

19. Conclusion (lines 517-519): “Additionally, we show that cancer genes that can be uncovered by their pathway centrality are different depending on the graphlet eigencentrality”. Different as in what? Described mechanisms? Please, rephrase to clarify.

20. Conclusion (line 524): Delete “using networkss”.

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Reviewer #1: No

Reviewer #2: No

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Graphlet eigencentralities capture novel central roles of genes in pathways

PONE-D-21-17376R1

Dear Dr. windels,

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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

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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

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3. Has the statistical analysis been performed appropriately and rigorously?

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 #2: Yes

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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

**********

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 comments have been addressed. The compiled revised PDF version still had a blurry version of Figure 1. Make sure the correct, sharpened version is uploaded to the system.

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Reviewer #2: Yes: Evangelos Karatzas