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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.
==Introduction==
Microarray data analysis involves several distinct steps, as outlined below. Changing any one of the steps has the potential to change the outcome of the analysis, so the MAQC Project<ref>{{cite web | url = http://www.fda.gov/nctr/science/centers/toxicoinformatics/maqc/ | title = MicroArray Quality Control (MAQC) Project | accessdate = 2007-12-26 | author = Dr. Leming Shi, National Center for Toxicological Research | publisher = U.S. Food and Drug Administration }}</ref> was created to identify a set of standard strategies.==Creating raw data==
Most microarray manufacturers, such as [[Affymetrix]], provide commercial data analysis software with microarray equipment such as plate readers. Raw Affy data contains information about mismatch spots, spots which do not precisely match the target sequence. These can theoretically measure the amount of nonspecific binding for a given target, but some popular approaches like RMA do not take advantage of them.<ref>{{cite journal |author=Bolstad BM, Irizarry RA, Astrand M, Speed TP |title=A comparison of normalization methods for high density oligonucleotide array data based on variance and bias |journal=Bioinformatics |volume=19 |issue=2 |pages=185–93 |year=2003 |pmid=12538238 |doi=}}</ref>
==Background correction==
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