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Normalized values with Robust Multichip Average background correction, quartile normalization, and median polish were calculated with the R/bioconductor package AffylmGUI (Irizarry et al. 2003).

Low-level analyses included background correction, quartile normalization and expression estimation using GCRMA [13].

Robust multi-array average (RMA) background correction and quartile normalization were used to adjust signal intensity data.

Public data were used in their available normalized form, since background correction and normalization had already been performed.

Cell files were normalized using the Partek Genomics Suite (RMA background correction, quantile normalization, median polishing).

The preliminary data processing includes background correction, within array normalization, and between array normalization.

In fact, this technique can be combined with certain normalization techniques such as Multiplicative Scatter correction (MSC) to perform background correction and normalization simultaneously.

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.

We used RMA and Bioconductor in R [70] for background correction and normalization [71], [72].

The normalization feature was employed to perform background correction, quantile normalization across gene chips, log base 2 transformation, and median polish summarization.