Microarray analysis techniques: Difference between revisions

Revision as of 01:50, 4 November 2006

Revision as of 20:59, 7 December 2006

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[[Image:Microarray2.gif|thumb|350px|Example of an approximately 40,000 probe spotted oligo microarray with enlarged inset to show detail.]]

'''Microarray analysis techniques''' are used in interpreting the data generated from experiments on DNA, RNA, and protein [[microarray]]s, which allow researchers to investigate the expression state of a large number of genes - in many cases, an organism's entire [[genome]] - in a single experiment. Such experiments generate a very large volume of genetic data that can be difficult to analyze, especially in the absence of good gene annotation. Most microarray manufacturers, such as [[Affymetrix]], provide commercial data analysis software with microarray equipment such as plate readers. Non-commercial tools such as [http://www.genmapp.org [GenMAPP]] also aid in organizing and visualizing gene network data procured from one or several microarray experiments.

Specialized software tools for statistical analysis to determine the extent of over- or under-expression of a gene in a microarray experiment relative to a reference state have also been developed to aid in identifying genes or gene sets associated with particular [[phenotype]]s. One such method of analysis, known as Gene Set Enrichment Analysis (GSEA), uses a [[Kolmogorov-Smirnov]]-style statistic to identify groups of genes that are regulated together{{ref|Subramanian}}. This third-party statistics package offers the user information on the genes or gene sets of interest, including links to entries in databases such as NCBI's [[GenBank]] and curated databases such as [http://www.biocarta.com Biocarta] and [http://www.geneontology.com [Gene Ontology]].

==References==