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===Identification of significant differential expression===
Many strategies exist to identify array probes that 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 twofold between treatment groups. More sophisticated 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 |vauthors=Shi L, Reid LH, Jones WD, etal |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 |pmc=3272078}}</ref><ref>{{cite journal |vauthors=Guo L, Lobenhofer EK, Wang C, etal |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=10.1038/nbt1238|s2cid=8192240 }}</ref> This represents an extremely important observation, since the point of performing experiments has to do with predicting general behavior. The MAQC group recommends using a fold change assessment plus a non-stringent p-value cutoff, further pointing out that changes in the background correction and scaling process have only a minimal impact on the rank order of fold change differences, but a substantial impact on p-values.{{cn|date=March 2023}}
=== Clustering ===
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