IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0188016.html
   My bibliography  Save this article

Estimation of the proteomic cancer co-expression sub networks by using association estimators

Author

Listed:
  • Cihat Erdoğan
  • Zeyneb Kurt
  • Banu Diri

Abstract

In this study, the association estimators, which have significant influences on the gene network inference methods and used for determining the molecular interactions, were examined within the co-expression network inference concept. By using the proteomic data from five different cancer types, the hub genes/proteins within the disease-associated gene-gene/protein-protein interaction sub networks were identified. Proteomic data from various cancer types is collected from The Cancer Proteome Atlas (TCPA). Correlation and mutual information (MI) based nine association estimators that are commonly used in the literature, were compared in this study. As the gold standard to measure the association estimators’ performance, a multi-layer data integration platform on gene-disease associations (DisGeNET) and the Molecular Signatures Database (MSigDB) was used. Fisher's exact test was used to evaluate the performance of the association estimators by comparing the created co-expression networks with the disease-associated pathways. It was observed that the MI based estimators provided more successful results than the Pearson and Spearman correlation approaches, which are used in the estimation of biological networks in the weighted correlation network analysis (WGCNA) package. In correlation-based methods, the best average success rate for five cancer types was 60%, while in MI-based methods the average success ratio was 71% for James-Stein Shrinkage (Shrink) and 64% for Schurmann-Grassberger (SG) association estimator, respectively. Moreover, the hub genes and the inferred sub networks are presented for the consideration of researchers and experimentalists.

Suggested Citation

  • Cihat Erdoğan & Zeyneb Kurt & Banu Diri, 2017. "Estimation of the proteomic cancer co-expression sub networks by using association estimators," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-19, November.
  • Handle: RePEc:plo:pone00:0188016
    DOI: 10.1371/journal.pone.0188016
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0188016
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0188016&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0188016?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Valur Emilsson & Gudmar Thorleifsson & Bin Zhang & Amy S. Leonardson & Florian Zink & Jun Zhu & Sonia Carlson & Agnar Helgason & G. Bragi Walters & Steinunn Gunnarsdottir & Magali Mouy & Valgerdur Ste, 2008. "Genetics of gene expression and its effect on disease," Nature, Nature, vol. 452(7186), pages 423-428, March.
    2. Eric E. Schadt, 2009. "Molecular networks as sensors and drivers of common human diseases," Nature, Nature, vol. 461(7261), pages 218-223, September.
    3. Joeri Ruyssinck & Vân Anh Huynh-Thu & Pierre Geurts & Tom Dhaene & Piet Demeester & Yvan Saeys, 2014. "NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-13, March.
    4. Ricardo de Matos Simoes & Frank Emmert-Streib, 2011. "Influence of Statistical Estimators of Mutual Information and Data Heterogeneity on the Inference of Gene Regulatory Networks," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-14, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Valur Emilsson & Elias F. Gudmundsson & Thorarinn Jonmundsson & Brynjolfur G. Jonsson & Michael Twarog & Valborg Gudmundsdottir & Zhiguang Li & Nancy Finkel & Stephen Poor & Xin Liu & Robert Esterberg, 2022. "A proteogenomic signature of age-related macular degeneration in blood," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Pi-Jing Wei & Di Zhang & Hai-Tao Li & Junfeng Xia & Chun-Hou Zheng, 2017. "DriverFinder: A Gene Length-Based Network Method to Identify Cancer Driver Genes," Complexity, Hindawi, vol. 2017, pages 1-10, August.
    3. Xue Jiang & Han Zhang & Xiongwen Quan & Zhandong Liu & Yanbin Yin, 2017. "Disease-related gene module detection based on a multi-label propagation clustering algorithm," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-17, May.
    4. Wei, Daijun & Deng, Xinyang & Zhang, Xiaoge & Deng, Yong & Mahadevan, Sankaran, 2013. "Identifying influential nodes in weighted networks based on evidence theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2564-2575.
    5. Yoo-Ah Kim & Stefan Wuchty & Teresa M Przytycka, 2011. "Identifying Causal Genes and Dysregulated Pathways in Complex Diseases," PLOS Computational Biology, Public Library of Science, vol. 7(3), pages 1-13, March.
    6. Susan Dina Ghiassian & Jörg Menche & Albert-László Barabási, 2015. "A DIseAse MOdule Detection (DIAMOnD) Algorithm Derived from a Systematic Analysis of Connectivity Patterns of Disease Proteins in the Human Interactome," PLOS Computational Biology, Public Library of Science, vol. 11(4), pages 1-21, April.
    7. Wu, Hongqian & Deng, Hongzhong & Li, Jichao & Wang, Yangjun & Yang, Kewei, 2024. "Hunting for influential nodes based on radiation theory in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 188(C).
    8. Dawei Li & Zedong Geng & Shixuan Xia & Hui Feng & Xiuhan Jiang & Hui Du & Pei Wang & Qun Lian & Yanhui Zhu & Yuxin Jia & Yao Zhou & Yaoyao Wu & Chenglong Huang & Guangtao Zhu & Yi Shang & Huihui Li & , 2024. "Integrative multi-omics analysis reveals genetic and heterotic contributions to male fertility and yield in potato," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    9. Guiqiong Xu & Chen Dong & Lei Meng, 2022. "Research on the Collaborative Innovation Relationship of Artificial Intelligence Technology in Yangtze River Delta of China: A Complex Network Perspective," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
    10. Seyed Yahya Anvar & Allan Tucker & Veronica Vinciotti & Andrea Venema & Gert-Jan B van Ommen & Silvere M van der Maarel & Vered Raz & Peter A C ‘t Hoen, 2011. "Interspecies Translation of Disease Networks Increases Robustness and Predictive Accuracy," PLOS Computational Biology, Public Library of Science, vol. 7(11), pages 1-14, November.
    11. Calvin McCarter & Judie Howrylak & Seyoung Kim, 2020. "Learning gene networks underlying clinical phenotypes using SNP perturbation," PLOS Computational Biology, Public Library of Science, vol. 16(10), pages 1-24, October.
    12. Elin Grundberg & Veronique Adoue & Tony Kwan & Bing Ge & Qing Ling Duan & Kevin C L Lam & Vonda Koka & Andreas Kindmark & Scott T Weiss & Kelan Tantisira & Hans Mallmin & Benjamin A Raby & Olle Nilsso, 2011. "Global Analysis of the Impact of Environmental Perturbation on cis-Regulation of Gene Expression," PLOS Genetics, Public Library of Science, vol. 7(1), pages 1-17, January.
    13. Frank Emmert-Streib & Galina V Glazko, 2011. "Pathway Analysis of Expression Data: Deciphering Functional Building Blocks of Complex Diseases," PLOS Computational Biology, Public Library of Science, vol. 7(5), pages 1-6, May.
    14. Kannan Venkateshan & Tegner Jesper, 2016. "Adaptive input data transformation for improved network reconstruction with information theoretic algorithms," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 15(6), pages 507-520, December.
    15. Xu, Guiqiong & Meng, Lei, 2023. "A novel algorithm for identifying influential nodes in complex networks based on local propagation probability model," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    16. Andrea Pinna & Nicola Soranzo & Alberto de la Fuente, 2010. "From Knockouts to Networks: Establishing Direct Cause-Effect Relationships through Graph Analysis," PLOS ONE, Public Library of Science, vol. 5(10), pages 1-8, October.
    17. Gao, Cai & Wei, Daijun & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2013. "A modified evidential methodology of identifying influential nodes in weighted networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5490-5500.
    18. Lingfei Wang & Tom Michoel, 2017. "Efficient and accurate causal inference with hidden confounders from genome-transcriptome variation data," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-26, August.
    19. Eric P Xing & Ross E Curtis & Georg Schoenherr & Seunghak Lee & Junming Yin & Kriti Puniyani & Wei Wu & Peter Kinnaird, 2014. "GWAS in a Box: Statistical and Visual Analytics of Structured Associations via GenAMap," PLOS ONE, Public Library of Science, vol. 9(6), pages 1-19, June.
    20. Fei, Liguo & Zhang, Qi & Deng, Yong, 2018. "Identifying influential nodes in complex networks based on the inverse-square law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1044-1059.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0188016. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.