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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 an average of at least two fold 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><ref>{{cite journal |author=Guo L, Lobenhofer EK, Wang C, ''et al'' |title=Rat toxicogenomic study reveals analytical consistency across microarray platforms |journal=Nat. Biotechnol. |volume=24 |issue=9 |pages=1162–9 |year=2006 |pmid=17061323 |doi=}}</ref>
==Pattern recognition==
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