Microarray analysis techniques: Difference between revisions

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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 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= |work=}}</ref>

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. RMA is a normalization approach that does not take advantage of them, but still must summarize the perfect matches through [[median polish]].<ref>{{cite journal |author=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}}</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 |author=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>