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==Spot filtering==
Visual identification of local artifacts, such as printing or washing defects, may likewise suggest the removal of individual spots. This can take a substantial amount of time depending on the quality of array manufacture. In addition, some procedures call for the elimination of all spots with an expression value below a certain threshold.
==Aggregation and normalization==
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. LIMMA provides a set of tools for background correction and scalling, 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>
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