IDEAS home Printed from https://ideas.repec.org/a/bpj/ijbist/v6y2010i1n29.html
   My bibliography  Save this article

Statistical Methods for Comparative Phenomics Using High-Throughput Phenotype Microarrays

Author

Listed:
  • Sturino Joseph

    (Texas A&M University)

  • Zorych Ivan

    (Texas A&M University)

  • Mallick Bani

    (Texas A&M University)

  • Pokusaeva Karina

    (Texas A&M University)

  • Chang Ying-Ying

    (Texas A&M University)

  • Carroll Raymond J

    (Texas A&M University)

  • Bliznuyk Nikolay

    (Texas A&M University)

Abstract

We propose statistical methods for comparing phenomics data generated by the Biolog Phenotype Microarray (PM) platform for high-throughput phenotyping. Instead of the routinely used visual inspection of data with no sound inferential basis, we develop two approaches. The first approach is based on quantifying the distance between mean or median curves from two treatments and then applying a permutation test; we also consider a permutation test applied to areas under mean curves. The second approach employs functional principal component analysis. Properties of the proposed methods are investigated on both simulated data and data sets from the PM platform.

Suggested Citation

  • Sturino Joseph & Zorych Ivan & Mallick Bani & Pokusaeva Karina & Chang Ying-Ying & Carroll Raymond J & Bliznuyk Nikolay, 2010. "Statistical Methods for Comparative Phenomics Using High-Throughput Phenotype Microarrays," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-21, August.
    • Handle: RePEc:bpj:ijbist:v:6:y:2010:i:1:n:29
    DOI: 10.2202/1557-4679.1227
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1557-4679.1227
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1557-4679.1227?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. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Minna Vehkala & Mikhail Shubin & Thomas R Connor & Nicholas R Thomson & Jukka Corander, 2015. "Novel R Pipeline for Analyzing Biolog Phenotypic Microarray Data," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-14, March.
    2. Lea A I Vaas & Johannes Sikorski & Victoria Michael & Markus Göker & Hans-Peter Klenk, 2012. "Visualization and Curve-Parameter Estimation Strategies for Efficient Exploration of Phenotype Microarray Kinetics," PLOS ONE, Public Library of Science, vol. 7(4), pages 1-18, April.
    3. Livio Corain & Viatcheslav Melas & Andrey Pepelyshev & Luigi Salmaso, 2014. "New insights on permutation approach for hypothesis testing on functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 339-356, September.

    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. Juan Carlos Chávez & Felipe J. Fonseca & Manuel Gómez-Zaldívar, 2017. "Resoluciones de disputas comerciales y desempeño económico regional en México. (Commercial Disputes Resolution and Regional Economic Performance in Mexico)," Ensayos Revista de Economia, Universidad Autonoma de Nuevo Leon, Facultad de Economia, vol. 0(1), pages 79-93, May.
    2. Chen, Ray-Bing & Chen, Ying & Härdle, Wolfgang K., 2014. "TVICA—Time varying independent component analysis and its application to financial data," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 95-109.
    3. Yan Yu Chen & Chun-Cheih Chao & Fu-Chen Liu & Po-Chen Hsu & Hsueh-Fen Chen & Shih-Chi Peng & Yung-Jen Chuang & Chung-Yu Lan & Wen-Ping Hsieh & David Shan Hill Wong, 2013. "Dynamic Transcript Profiling of Candida albicans Infection in Zebrafish: A Pathogen-Host Interaction Study," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-16, September.
    4. Plat, Richard, 2009. "Stochastic portfolio specific mortality and the quantification of mortality basis risk," Insurance: Mathematics and Economics, Elsevier, vol. 45(1), pages 123-132, August.
    5. Kondylis, Athanassios & Whittaker, Joe, 2008. "Spectral preconditioning of Krylov spaces: Combining PLS and PC regression," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2588-2603, January.
    6. Simplice A. Asongu & Nicholas M. Odhiambo, 2019. "Governance, capital flight and industrialisation in Africa," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), vol. 8(1), pages 1-22, December.
    7. M. J. Aziakpono & S. Kleimeier & H. Sander, 2012. "Banking market integration in the SADC countries: evidence from interest rate analyses," Applied Economics, Taylor & Francis Journals, vol. 44(29), pages 3857-3876, October.
    8. Bianca Maria Colosimo & Luca Pagani & Marco Grasso, 2024. "Modeling spatial point processes in video-imaging via Ripley’s K-function: an application to spatter analysis in additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 429-447, January.
    9. Ouyang, Yaofu & Li, Peng, 2018. "On the nexus of financial development, economic growth, and energy consumption in China: New perspective from a GMM panel VAR approach," Energy Economics, Elsevier, vol. 71(C), pages 238-252.
    10. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    11. Ionela Munteanu & Adriana Grigorescu & Elena Condrea & Elena Pelinescu, 2020. "Convergent Insights for Sustainable Development and Ethical Cohesion: An Empirical Study on Corporate Governance in Romanian Public Entities," Sustainability, MDPI, vol. 12(7), pages 1-17, April.
    12. Daniel Boss & Annick Hoffmann & Benjamin Rappaz & Christian Depeursinge & Pierre J Magistretti & Dimitri Van de Ville & Pierre Marquet, 2012. "Spatially-Resolved Eigenmode Decomposition of Red Blood Cells Membrane Fluctuations Questions the Role of ATP in Flickering," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-10, August.
    13. Doukas, Haris & Papadopoulou, Alexandra & Savvakis, Nikolaos & Tsoutsos, Theocharis & Psarras, John, 2012. "Assessing energy sustainability of rural communities using Principal Component Analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 1949-1957.
    14. Paschalis Arvanitidis & Athina Economou & Christos Kollias, 2016. "Terrorism’s effects on social capital in European countries," Public Choice, Springer, vol. 169(3), pages 231-250, December.
    15. Rizvi, Syed Kumail Abbas & Rahat, Birjees & Naqvi, Bushra & Umar, Muhammad, 2024. "Revolutionizing finance: The synergy of fintech, digital adoption, and innovation," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    16. Teerachai Amnuaylojaroen & Pavinee Chanvichit, 2024. "Historical Analysis of the Effects of Drought on Rice and Maize Yields in Southeast Asia," Resources, MDPI, vol. 13(3), pages 1-18, March.
    17. -, 2015. "The effects of climate change on the coasts of Latin America and the Caribbean: Climate variability, dynamics and trends," Documentos de Proyectos 39866, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL).
    18. Dorota Toczydlowska & Gareth W. Peters & Man Chung Fung & Pavel V. Shevchenko, 2017. "Stochastic Period and Cohort Effect State-Space Mortality Models Incorporating Demographic Factors via Probabilistic Robust Principal Components," Risks, MDPI, vol. 5(3), pages 1-77, July.
    19. Weili Duan & Bin He & Daniel Nover & Guishan Yang & Wen Chen & Huifang Meng & Shan Zou & Chuanming Liu, 2016. "Water Quality Assessment and Pollution Source Identification of the Eastern Poyang Lake Basin Using Multivariate Statistical Methods," Sustainability, MDPI, vol. 8(2), pages 1-15, January.
    20. Joanna Jasnos, 2021. "Hydrogeochemical Characteristics of Geothermal Waters from Mesozoic Formations in the Basement of the Central Part of the Carpathian Foredeep and the Carpathians (Poland) Using Multivariate Statistica," Energies, MDPI, vol. 14(13), pages 1-31, July.

    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:ijbist:v:6:y:2010:i:1:n:29. 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.