Prior biological knowledge-based approaches for the analysis of genome-wide expression profiles using gene sets and pathways | Zendy

Michael C. Wu | Zendy; Xihong Lin | Zendy

Open Access

Prior biological knowledge-based approaches for the analysis of genome-wide expression profiles using gene sets and pathways

Author(s) -

Michael C. Wu,

Xihong Lin

Publication year - 2009

Publication title -

statistical methods in medical research

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.952

H-Index - 85

eISSN - 1477-0334

pISSN - 0962-2802

DOI - 10.1177/0962280209351925

Subject(s) - biological data , microarray analysis techniques , computational biology , set (abstract data type) , computer science , principal component analysis , biological pathway , representation (politics) , data set , data mining , kernel (algebra) , gene , gene chip analysis , dna microarray , gene expression , bioinformatics , biology , artificial intelligence , genetics , mathematics , combinatorics , politics , political science , law , programming language

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.

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