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Comparing two different arrays, or two different samples hybridized to the same array generally involves making adjustments for systematic errors introduced by differences in procedures and dye intensity effects. In the case of Affymetrics arrays, there are multiple probesets on an array for the same target sequence, requiring some sort of summarization. The RMA method uses [[median polish]] as opposed to a straight average. Dye normaization for two color arrays is often acheived by [[local regression]]. Quantile normalization, also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.
==Identification of significant differential expression==
Many strategies exist to idenitfy which array probes show an unusual level of over expression or under expression. The simplest one is to call "significant" any probe that differs by at least two fold, on average between treatment groups. More sophicated approaches are often related to [[t test]]s or other mechanisms that take both effect size and variability into account. Curiously, the p-values associated with particular genes do not reproduce well between replicate experiments, and lists generated by straight fold change perform much better.<ref>{{cite journal |author=Shi L, Reid LH, Jones WD, ''et al'' |title=The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements |journal=Nat. Biotechnol. |volume=24 |issue=9 |pages=1151–61 |year=2006 |pmid=16964229 |doi=10.1038/nbt1239}}</ref>
==Pattern recognition==
Commercial systems for gene network analysis such as Ingenuity <ref> {{cite web |url=http://www.ingenuity.com/ |title=Ingenuity Systems |accessdate=2007-12-31 |format= |work=}} </ref> and Pathway studio<ref>{{cite web |url=http://www.ariadnegenomics.com/products/pathway-studio/ |title=Ariadne Genomics: Pathway Studio |accessdate=2007-12-31 |format= |work=}}</ref> 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 <ref>[{{cite web |url=http://www-stat.stanford.edu/~tibs/SAM/ |title=Significance Analysis of Microarrays |accessdate=2007-12-31 |format= |work=}}]</ref> are available through Stanford University. Another set is available from Harvard and MIT<ref>{{cite web |url=http://www.broad.mit.edu/tools/software.html |title=Software - Broad |accessdate=2007-12-31 |format= |work=}}</ref>
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