Public data were used in their available normalized form, since background correction and normalization had already been performed.
In fact, this technique can be combined with certain normalization techniques such as Multiplicative Scatter correction (MSC) to perform background correction and normalization simultaneously.
Generally, background correction and normalization are used to reduce the impact of variations in experimental conditions.
Background correction and normalization was performed using the robust multi-array average (RMA) algorithm.
We used RMA and Bioconductor in R [70] for background correction and normalization [71], [72].
After background correction and normalization with an unspecific band the pixel intensity of each protein was related to the corresponding control protein band.
The normexp and quantile methods in limma package [50] were used to perform the background correction and normalization between arrays, respectively.
It offers methods for computing expression values from mapped reads and raw microarray data, background correction and normalization and linking microarray probes to genomic coordinates.
Rmodel uses standard approaches to background correction and normalization (Figure 2A) [27] [28], [28], and finds segmentations in a plot of the base-2 logarithm of individual probe expression ratios (Figure 2B).
We compared three background correction and normalization methods, MAS5, Robust Multi-array Average, and gcRMA.
Analysis of images, background correction and normalization of data was performed as described previously [ 33].
