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Firstly, we applied the Robust Multichip Average (RMA) algorithm for background correction, normalisation and expression-level summarisation of the arrays (see above).
Background correction, normalisation and summarisation are provided by the RMA package, [ 11], resulting in logarithmised gene expression estimates, which can be used for the next processing step.
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The processing steps include background correction, normalization and probeset summarization.
The provided data set has already been processed (with background correction, quantile normalisation and log transformation).
RMA procedures were applied for background correction, quantile normalisation and summarisation.
Expression values were produced from probe in intensities using the expresso function with, unless stated otherwise, RMA background correction, quantile normalisation, and median-polish summarisation over probesets.
Background correction and normalisation were performed in the R computing environment (Ihaka and Gentleman, 1996) using the robust multi-array average algorithm.
Both background correction and normalisation were done with RMA (Robust Multichip Average) algorithm (Irizarry et al, 2003).
Background correction and normalisation of the dataset have been performed using the Affy package of Bioconductor (RMA algorithm) in the statistical environment, R (version 2.10.1).
Expression data from 36 OVCA cell lines were subjected to background correction and normalisation using the Robust Multichip Average algorithm in the Affymetrix Expression Console (http://www.affymetrix.com).
Single dye labeling of samples, hybridization procedures, data acquisition, background correction and normalisation were performed at the PartnerChip facilities following the standard protocol defined by NimbleGen [ 103, 104].
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