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The summarization of the data was performed with the RMA algorithm.
GCRMA [ 31] was used for quantile normalization and summarization of the data to generate expression scores in the log2 scale.
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The RMA statistical algorithm [77], available under the Bioconductor software package of R, was used for background noise removal, normalization and summarization of the microarray data.
Background adjustment, normalization and summarization of the microarray data was done using the GCRMA method [ 34].
Background adjustment, normalization and summarization of the microarray data were performed using the GCRMA method.
Nimblescan software was used to perform Robust Multi-array Analysis (RMA) for quantile normalization and probe-level summarization of the expression data, with normalization occurring first between arrays of the same sample type and then across all samples.
Data analysis was conducted using either Genespring GX v 7.3.1 (Agilent Technologies, UK) or the R statistical package [ 28] and Bioconductor [ 29]. Background correction, scaling and summarization of the raw data to generate expression values were done with the MAS5 algorithm.
A summarization box plot of the data set was shown in Additional File 1: Supplemental Figure S1.
After summarization and normalization of the data, clear spatial patterns can be seen in several of the samples, and the patterns are correlated with the technical variable of processing date.
By a critical review and summarization of published data, the current knowledge of the molecular basis underlying mOGCT are presented.
The RMA method [19] consists of three steps: background adjustment, quantile normalization [20], and probe set summarization of the log-normalized data applying a median polishing procedure.
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