IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v18y2019i2p13n3.html
   My bibliography  Save this article

A multivariate linear model for investigating the association between gene-module co-expression and a continuous covariate

Author

Listed:
  • Padayachee Trishanta

    (Hasselt University, I-BioStat, Diepenbeek, Belgium)

  • Khamiakova Tatsiana

    (Hasselt University, I-BioStat, Diepenbeek, Belgium)

  • Shkedy Ziv

    (Hasselt University, I-BioStat, Diepenbeek, Belgium)

  • Salo Perttu

    (Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland)

  • Perola Markus

    (Unit of Public Health Genomics, National Institute for Health and Welfare, Helsinki, Finland)

  • Burzykowski Tomasz

    (Hasselt University, I-BioStat, Diepenbeek, Belgium)

Abstract

A way to enhance our understanding of the development and progression of complex diseases is to investigate the influence of cellular environments on gene co-expression (i.e. gene-pair correlations). Often, changes in gene co-expression are investigated across two or more biological conditions defined by categorizing a continuous covariate. However, the selection of arbitrary cut-off points may have an influence on the results of an analysis. To address this issue, we use a general linear model (GLM) for correlated data to study the relationship between gene-module co-expression and a covariate like metabolite concentration. The GLM specifies the gene-pair correlations as a function of the continuous covariate. The use of the GLM allows for investigating different (linear and non-linear) patterns of co-expression. Furthermore, the modeling approach offers a formal framework for testing hypotheses about possible patterns of co-expression. In our paper, a simulation study is used to assess the performance of the GLM. The performance is compared with that of a previously proposed GLM that utilizes categorized covariates. The versatility of the model is illustrated by using a real-life example. We discuss the theoretical issues related to the construction of the test statistics and the computational challenges related to fitting of the proposed model.

Suggested Citation

  • Padayachee Trishanta & Khamiakova Tatsiana & Shkedy Ziv & Salo Perttu & Perola Markus & Burzykowski Tomasz, 2019. "A multivariate linear model for investigating the association between gene-module co-expression and a continuous covariate," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(2), pages 1-13, April.
    • Handle: RePEc:bpj:sagmbi:v:18:y:2019:i:2:p:13:n:3
    DOI: 10.1515/sagmb-2018-0008
    as

    Download full text from publisher

    File URL: https://doi.org/10.1515/sagmb-2018-0008
    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-2018-0008?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. Trishanta Padayachee & Tatsiana Khamiakova & Ziv Shkedy & Markus Perola & Perttu Salo & Tomasz Burzykowski, 2016. "The Detection of Metabolite-Mediated Gene Module Co-Expression Using Multivariate Linear Models," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-17, February.
    2. Peter Libby, 2002. "Inflammation in atherosclerosis," Nature, Nature, vol. 420(6917), pages 868-874, December.
    3. Steffen Fieuws & Geert Verbeke, 2006. "Pairwise Fitting of Mixed Models for the Joint Modeling of Multivariate Longitudinal Profiles," Biometrics, The International Biometric Society, vol. 62(2), pages 424-431, June.
    4. Gregory Wilding & Xueya Cai & Alan Hutson & Zhangsheng Yu, 2011. "A linear model-based test for the heterogeneity of conditional correlations," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2355-2366.
    5. Arne Henningsen & Ott Toomet, 2011. "maxLik: A package for maximum likelihood estimation in R," Computational Statistics, Springer, vol. 26(3), pages 443-458, September.
    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. Chuan Hong & Yang Ning & Peng Wei & Ying Cao & Yong Chen, 2017. "A semiparametric model for vQTL mapping," Biometrics, The International Biometric Society, vol. 73(2), pages 571-581, June.
    2. Marit Skogstad & Asgeir Mamen & Lars-Kristian Lunde & Bente Ulvestad & Dagfinn Matre & Hans Christian D. Aass & Reidun Øvstebø & Pia Nielsen & Kari N. Samuelsen & Øivind Skare & Per Anton Sirnes, 2019. "Shift Work Including Night Work and Long Working Hours in Industrial Plants Increases the Risk of Atherosclerosis," IJERPH, MDPI, vol. 16(3), pages 1-11, February.
    3. Maness, Michael & Cirillo, Cinzia, 2016. "An indirect latent informational conformity social influence choice model: Formulation and case study," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 75-101.
    4. Jeonghye Choi & David R. Bell & Leonard M. Lodish, 2012. "Traditional and IS-Enabled Customer Acquisition on the Internet," Management Science, INFORMS, vol. 58(4), pages 754-769, April.
    5. Mazen Nassar & Refah Alotaibi & Ahmed Elshahhat, 2023. "Reliability Estimation of XLindley Constant-Stress Partially Accelerated Life Tests using Progressively Censored Samples," Mathematics, MDPI, vol. 11(6), pages 1-24, March.
    6. John-Fritz Thony & Jean Vaillant, 2022. "Parameter Estimation for a Fractional Black–Scholes Model with Jumps from Discrete Time Observations," Mathematics, MDPI, vol. 10(22), pages 1-17, November.
    7. Badamasi Abba & Hong Wang, 2024. "A new failure times model for one and two failure modes system: A Bayesian study with Hamiltonian Monte Carlo simulation," Journal of Risk and Reliability, , vol. 238(2), pages 304-323, April.
    8. Anne Mette Fisker Hag & Sune Folke Pedersen & Christina Christoffersen & Tina Binderup & Mette Munk Jensen & Jesper Tranekjær Jørgensen & Dorthe Skovgaard & Rasmus Sejersten Ripa & Andreas Kjaer, 2012. "18F-FDG PET Imaging of Murine Atherosclerosis: Association with Gene Expression of Key Molecular Markers," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-9, November.
    9. Christopher H. Morrell & Larry J. Brant & Shan Sheng & E. Jeffrey Metter, 2012. "Screening for prostate cancer using multivariate mixed-effects models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(6), pages 1151-1175, November.
    10. Logar, Ivana & Brouwer, Roy & Campbell, Danny, 2020. "Does attribute order influence attribute-information processing in discrete choice experiments?," Resource and Energy Economics, Elsevier, vol. 60(C).
    11. Salvatore Santo Signorelli & Elisa Marino & Salvatore Scuto, 2019. "Inflammation and Peripheral Arterial Disease," J, MDPI, vol. 2(2), pages 1-10, April.
    12. Wang, Shengjie & Kang, Yanfei & Petropoulos, Fotios, 2024. "Combining probabilistic forecasts of intermittent demand," European Journal of Operational Research, Elsevier, vol. 315(3), pages 1038-1048.
    13. Refah Alotaibi & Mazen Nassar & Hoda Rezk & Ahmed Elshahhat, 2022. "Inferences and Engineering Applications of Alpha Power Weibull Distribution Using Progressive Type-II Censoring," Mathematics, MDPI, vol. 10(16), pages 1-21, August.
    14. Muhammet Burak Kılıç & Yusuf Şahin & Melih Burak Koca, 2021. "Genetic algorithm approach with an adaptive search space based on EM algorithm in two-component mixture Weibull parameter estimation," Computational Statistics, Springer, vol. 36(2), pages 1219-1242, June.
    15. Karl, Andrew T. & Yang, Yan & Lohr, Sharon L., 2014. "Computation of maximum likelihood estimates for multiresponse generalized linear mixed models with non-nested, correlated random effects," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 146-162.
    16. Manisera, Marica & Zuccolotto, Paola, 2014. "Modeling rating data with Nonlinear CUB models," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 100-118.
    17. Cailan Lindsay Feingold & Abbas Smiley, 2022. "Healthy Sleep Every Day Keeps the Doctor Away," IJERPH, MDPI, vol. 19(17), pages 1-35, August.
    18. Wen Kong & Xun Niu & Tianshu Zeng & Meixia Lu & Lulu Chen, 2015. "Impact of Treatment with Metformin on Adipocytokines in Patients with Polycystic Ovary Syndrome: A Meta-Analysis," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-20, October.
    19. Chiara Brombin & Luigi Salmaso & Lara Fontanella & Luigi Ippoliti, 2015. "Nonparametric combination-based tests in dynamic shape analysis," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(4), pages 460-484, December.
    20. Nonanzit Pérez-Hernández & Gilberto Vargas-Alarcón & Rosalinda Posadas-Sánchez & Nancy Martínez-Rodríguez & Carlos Alfonso Tovilla-Zárate & Adrián Asael Rodríguez-Cortés & Oscar Pérez-Méndez & Ruben B, 2016. "PHACTR1 Gene Polymorphism Is Associated with Increased Risk of Developing Premature Coronary Artery Disease in Mexican Population," IJERPH, MDPI, vol. 13(8), pages 1-9, August.

    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:2:p:13:n:3. 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.