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

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  • 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
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    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.
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    Cited by:

    1. 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.
    2. 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.
    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).

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