title: Pathways to the analysis of microarray data.
abstract: The development of microarray technology allows the simultaneous measurement of the expression of many thousands of genes. The information gained offers an unprecedented opportunity to fully characterize biological processes. However, this challenge will only be successful if new tools for the efficient integration and interpretation of large datasets are available. One of these tools, pathway analysis, involves looking for consistent but subtle changes in gene expression by incorporating either pathway or functional annotations. We review several methods of pathway analysis and compare the performance of three, the binomial distribution, z scores, and gene set enrichment analysis, on two microarray datasets. Pathway analysis is a promising tool to identify the mechanisms that underlie diseases, adaptive physiological compensatory responses and new avenues for investigation.
authors.affiliation: University of Cambridge Department of Clinical Biochemistry, Box 232, Addenbrooke's Hospital, Hills Road, Cambridge, UK, CB2 2QR. rkc24@cam.ac.uk
authors.forename: R Keira
authors.initials: RK
authors.lastname: Curtis
authors.forename: Matej
authors.initials: M
authors.lastname: Oresic
authors.forename: Antonio
authors.initials: A
authors.lastname: Vidal-Puig
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D005786:Gene Expression Regulation / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015398:Signal Transduction / Q000502:physiology*; D014157:Transcription Factors / Q000378:metabolism*
chemical_list: D014157:Transcription Factors