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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. Dye normalization for two color arrays is often achieved by [[local regression]]. LIMMA provides a set of tools for background correction and scaling, as well as an option to average on-slide duplicate spots.<ref>{{cite web |url=http://bioinf.wehi.edu.au/limma/ |title=LIMMA Library: Linear Models for Microarray Data |accessdate=2008-01-01 |format= |website=}}</ref> A common method for evaluating how well normalized an array is, is to plot an [[MA plot]] of the data. MA plots can be produced using programs and languages such as R, MATLAB, and Excel.
Raw Affy data contains about twenty probes for the same RNA target. Half of these are "mismatch spots", which do not precisely match the target sequence. These can theoretically measure the amount of nonspecific binding for a given target. Robust Multi-array Average (RMA) <ref>{{cite journal|last=Irizarry|first=RA|author2=Hobbs, B |author3=Collin, F |author4=Beazer-Barclay, YD |author5=Antonellis, KJ |author6=Scherf, U |author7= Speed, TP |title=Exploration, normalization, and summaries of high density oligonucleotide array probe level data.|journal=Biostatistics|volume=4|issue=2|pages=249–64|year=2003|pmid=12925520 |doi=10.1093/biostatistics/4.2.249|doi-access=free}}</ref> is a normalization approach that does not take advantage of these mismatch spots, but still must summarize the perfect matches through [[median polish]].<ref>{{cite journal |vauthors=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=10.1093/bioinformatics/19.2.185|doi-access=free }}</ref> The median polish algorithm, although robust, behaves differently depending on the number of samples analyzed.<ref>{{cite journal |vauthors=Giorgi FM, Bolger AM, Lohse M, Usadel B |title=Algorithm-driven Artifacts in median polish summarization of Microarray data |journal=BMC Bioinformatics |volume=11 |pages=553 |year=2010 |pmid=21070630 |doi=10.1186/1471-2105-11-553 |pmc=2998528}}</ref> Quantile normalization, also part of RMA, is one sensible approach to normalize a batch of arrays in order to make further comparisons meaningful.
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|doi-access=free }}</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| doi-access = free }}</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>{{Cite web | url=http://affycomp.jhsph.edu/ | title=Affycomp III: A Benchmark for Affymetrix GeneChip Expression Measures}}</ref> FARMS outperformed all other summarizations methods with respect to sensitivity and specificity.
===Identification of significant differential expression===
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==Error correction and quality control==
===Quality control===
Entire arrays may have obvious flaws detectable by visual inspection, pairwise comparisons to arrays in the same experimental group, or by analysis of RNA degradation.<ref>{{cite journal |vauthors=Wilson CL, Miller CJ |title=Simpleaffy: a BioConductor package for Affymetrix Quality Control and data analysis |journal=Bioinformatics |volume=21 |issue=18 |pages=3683–5 |year=2005 |pmid=16076888 |doi=10.1093/bioinformatics/bti605|doi-access=free }}</ref> Results may improve by removing these arrays from the analysis entirely.
===Background correction===
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