IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v10y2011i1n23.html
   My bibliography  Save this article

Analyzing Time-Course Microarray Data Using Functional Data Analysis - A Review

Author

Listed:
  • Coffey Norma
  • Hinde John

Abstract

Gene expression over time can be viewed as a continuous process and therefore represented as a continuous curve or function. Functional data analysis (FDA) is a statistical methodology used to analyze functional data that has become increasingly popular in the analysis of time-course gene expression data. Several FDA techniques have been applied to gene expression profiles including functional regression analysis (to describe the relationship between expression profiles and other covariate(s)), functional discriminant analysis (to discriminate and classify groups of genes) and functional principal components analysis (for dimension reduction and clustering). This paper reviews the use of FDA and its associated methods to analyze time-course microarray gene expression data.

Suggested Citation

  • Coffey Norma & Hinde John, 2011. "Analyzing Time-Course Microarray Data Using Functional Data Analysis - A Review," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-32, May.
  • Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:23
    DOI: 10.2202/1544-6115.1671
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1544-6115.1671
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    File URL: https://libkey.io/10.2202/1544-6115.1671?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. Müller, Hans-Georg & Yao, Fang, 2008. "Functional Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1534-1544.
    2. Fang Yao & Hans-Georg Müller & Andrew J. Clifford & Steven R. Dueker & Jennifer Follett & Yumei Lin & Bruce A. Buchholz & John S. Vogel, 2003. "Shrinkage Estimation for Functional Principal Component Scores with Application to the Population Kinetics of Plasma Folate," Biometrics, The International Biometric Society, vol. 59(3), pages 676-685, September.
    3. Ma, Ping & Zhong, Wenxuan, 2008. "Penalized Clustering of Large-Scale Functional Data With Multiple Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 625-636, June.
    4. F. Hong & H. Li, 2006. "Functional Hierarchical Models for Identifying Genes with Different Time-Course Expression Profiles," Biometrics, The International Biometric Society, vol. 62(2), pages 534-544, June.
    5. Fraley C. & Raftery A.E., 2002. "Model-Based Clustering, Discriminant Analysis, and Density Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 611-631, June.
    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. İstem Köymen Keser & İpek Deveci Kocakoç & Ali Kemal Şehirlioğlu, 2016. "A New Descriptive Statistic for Functional Data: Functional Coefficient of Variation," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 4(2), pages 1-10, September.
    2. Xiaoqi Jiang & Steven Wink & Bob van de Water & Annette Kopp-Schneider, 2017. "Functional analysis of high-content high-throughput imaging data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(11), pages 1903-1919, August.
    3. Aneiros, Germán & Horová, Ivana & Hušková, Marie & Vieu, Philippe, 2022. "On functional data analysis and related topics," Journal of Multivariate Analysis, Elsevier, vol. 189(C).

    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. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
    2. Shaikh Mateen & McNicholas Paul D & Desmond Anthony F, 2010. "A Pseudo-EM Algorithm for Clustering Incomplete Longitudinal Data," The International Journal of Biostatistics, De Gruyter, vol. 6(1), pages 1-17, March.
    3. Lian, Heng, 2010. "Sparse Bayesian hierarchical modeling of high-dimensional clustering problems," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1728-1737, August.
    4. Coffey, N. & Hinde, J. & Holian, E., 2014. "Clustering longitudinal profiles using P-splines and mixed effects models applied to time-course gene expression data," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 14-29.
    5. Farcomeni Alessio & Arima Serena, 2012. "A Bayesian autoregressive three-state hidden Markov model for identifying switching monotonic regimes in Microarray time course data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(4), pages 1-31, June.
    6. Zhu, Hanbing & Li, Rui & Zhang, Riquan & Lian, Heng, 2020. "Nonlinear functional canonical correlation analysis via distance covariance," Journal of Multivariate Analysis, Elsevier, vol. 180(C).
    7. Şentürk, Damla & Ghosh, Samiran & Nguyen, Danh V., 2014. "Exploratory time varying lagged regression: Modeling association of cognitive and functional trajectories with expected clinic visits in older adults," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 1-15.
    8. Ufuk Beyaztas & Han Lin Shang, 2021. "A partial least squares approach for function-on-function interaction regression," Computational Statistics, Springer, vol. 36(2), pages 911-939, June.
    9. Pourahmadi, Mohsen & Daniels, Michael J. & Park, Trevor, 2007. "Simultaneous modelling of the Cholesky decomposition of several covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 98(3), pages 568-587, March.
    10. Huang, Lele & Zhao, Junlong & Wang, Huiwen & Wang, Siyang, 2016. "Robust shrinkage estimation and selection for functional multiple linear model through LAD loss," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 384-400.
    11. Stefano Tonellato & Andrea Pastore, 2013. "On the comparison of model-based clustering solutions," Working Papers 2013:05, Department of Economics, University of Venice "Ca' Foscari".
    12. Scrucca, Luca, 2011. "Model-based SIR for dimension reduction," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 3010-3026, November.
    13. Klaus Ackermann & Simon D Angus & Paul A Raschky, 2020. "Estimating Sleep and Work Hours from Alternative Data by Segmented Functional Classification Analysis, SFCA," SoDa Laboratories Working Paper Series 2020-04, Monash University, SoDa Laboratories.
    14. Anthony C. Atkinson & Marco Riani & Andrea Cerioli, 2018. "Cluster detection and clustering with random start forward searches," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(5), pages 777-798, April.
    15. Di Zio, Marco & Guarnera, Ugo & Luzi, Orietta, 2007. "Imputation through finite Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5305-5316, July.
    16. Beibei Zhang & Rong Chen, 2018. "Nonlinear Time Series Clustering Based on Kolmogorov-Smirnov 2D Statistic," Journal of Classification, Springer;The Classification Society, vol. 35(3), pages 394-421, October.
    17. Sylvia Frühwirth‐Schnatter & Christoph Pamminger & Andrea Weber & Rudolf Winter‐Ebmer, 2012. "Labor market entry and earnings dynamics: Bayesian inference using mixtures‐of‐experts Markov chain clustering," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1116-1137, November.
    18. Hans-Georg Müller & Ying Zhang, 2005. "Time-Varying Functional Regression for Predicting Remaining Lifetime Distributions from Longitudinal Trajectories," Biometrics, The International Biometric Society, vol. 61(4), pages 1064-1075, December.
    19. Montanari, Angela & Viroli, Cinzia, 2011. "Maximum likelihood estimation of mixtures of factor analyzers," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2712-2723, September.
    20. Giovanna Devetag & Sibilla Guida & Luca Polonio, 2016. "An eye-tracking study of feature-based choice in one-shot games," Experimental Economics, Springer;Economic Science Association, vol. 19(1), pages 177-201, March.

    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:10:y:2011:i:1:n:23. 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.