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The current Affymetrix MAS5 algorithm, which uses both perfect match and mismatch probes, continues to enjoy popularity and do well in head to head tests.<ref>{{cite journal |vauthors=Lim WK, Wang K, Lefebvre C, Califano A |title=Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks |journal=Bioinformatics |volume=23 |issue=13 |pages=i282–8 |year=2007 |pmid=17646307 |doi=10.1093/bioinformatics/btm201}}</ref>
[[File:Mas5.jpg|thumb|500px|none|Flowchart showing how the MAS5 algorithm by Agilent works.]]
Factor Analysis for Robust Microarray Summarization (FARMS)<ref>{{cite journal | vauthors = Hochreiter S, Clevert DA, Obermayer K | year = 2006 | title = A new summarization method for affymetrix probe level data | url = http://bioinformatics.oxfordjournals.org/cgi/content/short/22/8/943 | journal = Bioinformatics | volume = 22 | issue = 8| pages = 943–949 | doi=10.1093/bioinformatics/btl033 | pmid=16473874}}</ref> is a model-based technique for summarizing array data at perfect match probe level. It is based on a factor analysis model for which a Bayesian maximum a posteriori method optimizes the model parameters under the assumption of Gaussian measurement noise. According to the Affycomp benchmark<ref>http://affycomp.jhsph.edu/</ref> FARMS outperformed all other summarizations methods with respect to sensitivity and specificity.
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