Preprocessing microarray data normally includes background correction, normalization and summation, while preprocessing RNA-Seq data includes artifact filtering and short read alignment/assembly.
Bioinformatics strategies were used for background correction, normalization, and data analysis of differentially methylated genomic regions between tumor, and normal tissue.
We used the minfi and bumphunter packages found in Bioconductor to perform background correction, normalization, and data analysis of differentially methylated genomic regions between tumor and normal tissue.
Topics include experimental design, detection, image processing, measurement errors, ratio calculation, background correction, normalization, and higher-level data processing.
The preprocessing procedure for the raw microarray data consists of background correction, normalization, and summarization.
The background correction, normalization and derivation of expression measures were based on the Affymetrix signal (MAS 5.0 algorithm).
Background correction, normalization and probe set summarization were performed using the robust multi array algorithm with background adjustment (gcrma, [35]).
Expression measurements were pre-processed to provide background correction, normalization and log base 2 transformation using RMA (Robust Multi-array Average) [77].
Background correction, normalization and data summary were performed with non linear methods using the rma function of the affy package [71].
CEL files were preprocessed using the RMA (Robust Multi-Array) method, a three-step process which integrates background correction, normalization and summarization of probe values.
Robust Multi-Array Average (RMA) [57] was used for background correction, normalization and calculation of expression values for all 18 samples from the probe intensity files.
