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This analysis method also helps the user to determine the distributional effect of background compensation and normalization and can thus help to guide the choice of these.
As part of this study we also introduce a new pre-processing method of microarray probe set intensity values that helps with the inspection of microarray quality and aides the choice of background compensation and normalization procedure.
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Separate R scripts for QLT normalization, Global background compensation and real time RT-PCR analysis were developed and can be obtained from authors on request.
Each microarray chip was hybridized to a single sample labeled with Cy3. Background subtraction and normalization were performed.
Briefly, images were opened and, after background subtraction and normalization, MYO10 spots were automatically detected using Michael Schmid's 'Find maxima' plugin.
The signal values were derived by background subtraction and normalization.
We used RMA and Bioconductor in R [70] for background correction and normalization [71], [72].
Background correction and normalization was performed using the robust multi-array average (RMA) algorithm.
Generally, background correction and normalization are used to reduce the impact of variations in experimental conditions.
Signal values were derived by background subtraction and normalization.
We compared three background correction and normalization methods, MAS5, Robust Multi-array Average, and gcRMA.
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