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'''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. Commercial systems for gene network analysis such as Ingenuity [http://www.ingenuity.com/] and Pathway studio create visual representations of differentially expressed genes based on current scientific literature. Non-commercial tools such as [[GenMAPP]] also aid in organizing and visualizing gene network data procured from one or several microarray experiments. A wide variety of microarray analysis tools are availible through [[Bioconductor]] written in the [[R programming language]]. The frequently cited SAM Excel module and other microarray tools [http://www-stat.stanford.edu/~tibs/SAM/] are available through Stanford University.
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 [[Gene Ontology]]. A related system, PAINT [http://www.dbi.tju.edu/dbi/tools/paint/] performs a statistical analysis on gene promoter regions, identifying over and under representation of previously identified [[transcription factor]] response elements.
==References==
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