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.
The RMA statistical algorithm [77], available under the Bioconductor software package of R, was used for background noise removal, normalization and summarization of microarray data corresponding to two independent experiments for each condition (cells starved for carbon for 30 and 60 min, and the respective controls).
In order to deal with Affymetrix SNP arrays it is required to apply several low level processes, namely, background removal, calibration, normalization and summarization [ 1].
The typical procedures of XAFS data reduction were followed in this study, and consist of averaging successive data scans, calibration to the energy of As(0) foil at 11,867.0 eV [54], background removal using linear or Gaussian functions, normalization to the "free atom" absorbance (i.e., Fermi level) at 11,885.0 eV and extraction of the extended XAFS (EXAFS) portion of the spectrum.
Background removal was performed following the producer's instructions and quantile normalization of raw data was carried out using Partek Genomic suite (St . Louis MO, USA).
Topics include experimental design, detection, image processing, measurement errors, ratio calculation, background correction, normalization, and higher-level data processing.
Background correction, normalization and probe set summarization were performed using the robust multi array algorithm with background adjustment (gcrma, [35]).
The preprocessing procedure for the raw microarray data consists of background correction, normalization, and summarization.
Microarray data were preprocessed (background subtraction, normalization, and summarization) using robust multi-chip analysis (RMA) [18].
The processing steps include background correction, normalization and probeset summarization.
