IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v18y2019i1p13n1.html
   My bibliography  Save this article

Meta-analytic framework for modeling genetic coexpression dynamics

Author

Listed:
  • Kinzy Tyler G.

    (Case Western Reserve University, Cleveland, USA)

  • Starr Timothy K.

    (University of Minnesota, Minneapolis, USA)

  • Tseng George C.

    (University of Pittsburgh, Pittsburgh, USA)

  • Ho Yen-Yi

    (Department of Statistics, University of South Carolina, Columbia, SC 29209, USA)

Abstract

Methods for exploring genetic interactions have been developed in an attempt to move beyond single gene analyses. Because biological molecules frequently participate in different processes under various cellular conditions, investigating the changes in gene coexpression patterns under various biological conditions could reveal important regulatory mechanisms. One of the methods for capturing gene coexpression dynamics, named liquid association (LA), quantifies the relationship where the coexpression between two genes is modulated by a third “coordinator” gene. This LA measure offers a natural framework for studying gene coexpression changes and has been applied increasingly to study regulatory networks among genes. With a wealth of publicly available gene expression data, there is a need to develop a meta-analytic framework for LA analysis. In this paper, we incorporated mixed effects when modeling correlation to account for between-studies heterogeneity. For statistical inference about LA, we developed a Markov chain Monte Carlo (MCMC) estimation procedure through a Bayesian hierarchical framework. We evaluated the proposed methods in a set of simulations and illustrated their use in two collections of experimental data sets. The first data set combined 10 pancreatic ductal adenocarcinoma gene expression studies to determine the role of possible coordinator gene USP9X in the Hippo pathway. The second experimental data set consisted of 907 gene expression microarray Escherichia coli experiments from multiple studies publicly available through the Many Microbe Microarray Database website (http://m3d.bu.edu/) and examined genes that coexpress with serA in the presence of coordinator gene Lrp.

Suggested Citation

  • Kinzy Tyler G. & Starr Timothy K. & Tseng George C. & Ho Yen-Yi, 2019. "Meta-analytic framework for modeling genetic coexpression dynamics," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(1), pages 1-13, February.
    • Handle: RePEc:bpj:sagmbi:v:18:y:2019:i:1:p:13:n:1
    DOI: 10.1515/sagmb-2017-0052
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/sagmb-2017-0052
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.1515/sagmb-2017-0052?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Vân Anh Huynh-Thu & Alexandre Irrthum & Louis Wehenkel & Pierre Geurts, 2010. "Inferring Regulatory Networks from Expression Data Using Tree-Based Methods," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-10, September.
    2. Pedro A. Pérez-Mancera & Alistair G. Rust & Louise van der Weyden & Glen Kristiansen & Allen Li & Aaron L. Sarver & Kevin A. T. Silverstein & Robert Grützmann & Daniela Aust & Petra Rümmele & Thomas K, 2012. "The deubiquitinase USP9X suppresses pancreatic ductal adenocarcinoma," Nature, Nature, vol. 486(7402), pages 266-270, June.
    3. John A. Dawson & Christina Kendziorski, 2012. "An Empirical Bayesian Approach for Identifying Differential Coexpression in High-Throughput Experiments," Biometrics, The International Biometric Society, vol. 68(2), pages 455-465, June.
    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. Alfonso Monaco & Nicola Amoroso & Loredana Bellantuono & Eufemia Lella & Angela Lombardi & Anna Monda & Andrea Tateo & Roberto Bellotti & Sabina Tangaro, 2019. "Shannon entropy approach reveals relevant genes in Alzheimer’s disease," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-29, December.
    2. Cecilia Pessoa Rodrigues & Aindrila Chatterjee & Meike Wiese & Thomas Stehle & Witold Szymanski & Maria Shvedunova & Asifa Akhtar, 2021. "Histone H4 lysine 16 acetylation controls central carbon metabolism and diet-induced obesity in mice," Nature Communications, Nature, vol. 12(1), pages 1-21, December.
    3. Jie Xiong & Tong Zhou, 2012. "Gene Regulatory Network Inference from Multifactorial Perturbation Data Using both Regression and Correlation Analyses," PLOS ONE, Public Library of Science, vol. 7(9), pages 1-13, September.
    4. Marco Grimaldi & Roberto Visintainer & Giuseppe Jurman, 2011. "RegnANN: Reverse Engineering Gene Networks Using Artificial Neural Networks," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-19, December.
    5. Marius Arend & Yizhong Yuan & M. Águila Ruiz-Sola & Nooshin Omranian & Zoran Nikoloski & Dimitris Petroutsos, 2023. "Widening the landscape of transcriptional regulation of green algal photoprotection," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    6. Takeshi Hase & Samik Ghosh & Ryota Yamanaka & Hiroaki Kitano, 2013. "Harnessing Diversity towards the Reconstructing of Large Scale Gene Regulatory Networks," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-16, November.
    7. Ruonan Wu & Michelle R. Davison & William C. Nelson & Montana L. Smith & Mary S. Lipton & Janet K. Jansson & Ryan S. McClure & Jason E. McDermott & Kirsten S. Hofmockel, 2023. "Hi-C metagenome sequencing reveals soil phage–host interactions," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    8. Li, Jiawen & Meng, Lu & Zhang, Zelin & Yang, Kejia, 2023. "Low-frequency, high-impact: Discovering important rare events from UGC," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    9. Yichi Niu & Jiayi Luo & Chenghang Zong, 2024. "Single-cell total-RNA profiling unveils regulatory hubs of transcription factors," Nature Communications, Nature, vol. 15(1), pages 1-18, December.
    10. Fei Liu & Shao-Wu Zhang & Wei-Feng Guo & Ze-Gang Wei & Luonan Chen, 2016. "Inference of Gene Regulatory Network Based on Local Bayesian Networks," PLOS Computational Biology, Public Library of Science, vol. 12(8), pages 1-17, August.
    11. 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.
    12. Zhen Yang & Yen‐Yi Ho, 2022. "Modeling dynamic correlation in zero‐inflated bivariate count data with applications to single‐cell RNA sequencing data," Biometrics, The International Biometric Society, vol. 78(2), pages 766-776, June.
    13. Mingyi Wang & Jerome Verdier & Vagner A Benedito & Yuhong Tang & Jeremy D Murray & Yinbing Ge & Jörg D Becker & Helena Carvalho & Christian Rogers & Michael Udvardi & Ji He, 2013. "LegumeGRN: A Gene Regulatory Network Prediction Server for Functional and Comparative Studies," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-7, July.
    14. Fei Wang & Peiwen Ding & Xue Liang & Xiangning Ding & Camilla Blunk Brandt & Evelina Sjöstedt & Jiacheng Zhu & Saga Bolund & Lijing Zhang & Laura P. M. H. Rooij & Lihua Luo & Yanan Wei & Wandong Zhao , 2022. "Endothelial cell heterogeneity and microglia regulons revealed by a pig cell landscape at single-cell level," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    15. Qiao Wen Tan & Peng Ken Lim & Zhong Chen & Asher Pasha & Nicholas Provart & Marius Arend & Zoran Nikoloski & Marek Mutwil, 2023. "Cross-stress gene expression atlas of Marchantia polymorpha reveals the hierarchy and regulatory principles of abiotic stress responses," Nature Communications, Nature, vol. 14(1), pages 1-19, December.
    16. Bastien Lextrait, 2021. "Scaling up SME's credit scoring scope with LightGBM," EconomiX Working Papers 2021-25, University of Paris Nanterre, EconomiX.
    17. Maghsoodi, Masoume, 2016. "A New Method to Build Gene Regulation Network Based on Fuzzy Hierarchical Clustering Methods," MPRA Paper 79743, University Library of Munich, Germany.
    18. Ze Yan & Ji Yang & Wen-Tian Wei & Ming-Liang Zhou & Dong-Xin Mo & Xing Wan & Rui Ma & Mei-Ming Wu & Jia-Hui Huang & Ya-Jing Liu & Feng-Hua Lv & Meng-Hua Li, 2024. "A time-resolved multi-omics atlas of transcriptional regulation in response to high-altitude hypoxia across whole-body tissues," Nature Communications, Nature, vol. 15(1), pages 1-22, December.
    19. Hyunje Yang & Honggeun Lim & Haewon Moon & Qiwen Li & Sooyoun Nam & Jaehoon Kim & Hyung Tae Choi, 2022. "Simple Optimal Sampling Algorithm to Strengthen Digital Soil Mapping Using the Spatial Distribution of Machine Learning Predictive Uncertainty: A Case Study for Field Capacity Prediction," Land, MDPI, vol. 11(11), pages 1-18, November.
    20. Natalie M. Clark & Trevor M. Nolan & Ping Wang & Gaoyuan Song & Christian Montes & Conner T. Valentine & Hongqing Guo & Rosangela Sozzani & Yanhai Yin & Justin W. Walley, 2021. "Integrated omics networks reveal the temporal signaling events of brassinosteroid response in Arabidopsis," Nature Communications, Nature, vol. 12(1), pages 1-13, December.

    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:bpj:sagmbi:v:18:y:2019:i:1:p:13:n:1. 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: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    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.