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 it¿s associated methods to analyze time-course microarray
gene expression data.
