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Expression values were computed and normalized using the RMA normalization of the cel file data employing the Affymetrix Expression Consol.
Background subtraction and normalization for both experiments was performed using the RMA normalization procedure [ 65, 66] and the three biological replicates were averaged.
Mouse data for postpartum testis development (GEO repository: GSE12769) and isolated testicular cells [ 19](ArrayExpress repository: E-TABM-130) were downloaded and pre-processed using the RMA normalization module with AMEN software.
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Thus, we concluded that both normalization methods performed similarly on our dataset and used the RMA-normalized data in the subsequent analyses.
The downloaded profile had been preprocessed which was carried out with background correction, log2 transformation, and quantile normalization using the RMA (Robust Multi-array Analysis) method of affy package in R [ 13].
In the context of high-dimensional data, two further important preparation steps often performed using the whole dataset are dimension reduction procedures such as Principal Component Analysis (PCA) and normalization for example normalization using the RMA ("Robust Multi-array Average") method [ 12] for microarray gene expression data.
Normalization was carried out using the RMA algorithm [44] as implemented in Bioconductor.
Normalization and background correction were performed using the rma algorithm of the affy package [ 42].
Extraction, normalization and computation of the expression indexes were performed using the RMA function of Bioconductor's affy package [ 48, 49].
Background correction, normalization and data summary were performed with non linear methods using the rma function of the affy package [71].
Background correction, normalization and data summary were performed with non linear methods using the rma function of the affy package [ 25].
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