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

被引：22
|
作者：
Coffey, Norma
Hinde, John
机构：
[1] National University of Ireland, Galway
来源：
STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY | 2011年 / 10卷 / 01期
基金：
爱尔兰科学基金会;
关键词：
functional data analysis; time-course microarray data; gene expression; GENE-EXPRESSION; MODEL; CLASSIFICATION;
D O I：
10.2202/1544-6115.1671
中图分类号：
Q5 [生物化学]; Q7 [分子生物学];
学科分类号：
071010 ; 081704 ;
摘要：
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.
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页数：33
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