Prior biological knowledge-based approaches for the analysis of genome-wide expression profiles using gene sets and pathways.
An increasing challenge in analysis of microarray data is how to interpret and gain biological insight of profiles of thousands of genes. This article provides a review of statistical methods for analysis of microarray data by incorporating prior biological knowledge using gene sets and biological pathways, which consist of groups of biologically similar genes. We first discuss issues of individual gene analysis. We compare several methods for analysis of gene sets including over-representation anlaysis, gene set enrichment analysis, principal component analysis, global test and kernel machine. We discuss the assumptions of these methods and their pros and cons. We illustrate these methods by application to a type II diabetes data set.
Wu
Michael C
MC
Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA. mwu@hsph.harvard.edu
Lin
Xihong
X
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D003924:Diabetes Mellitus, Type 2 / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods / Q000706:statistics & numerical data*; D016678:Genome; D006801:Humans; D051188:Knowledge Bases; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods