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We have investigated several normalization methods, including quantile, mean, and median normalization methods, and endogenous controls identified using various stability criteria.
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Microarray data were normalized using median normalization method.
Gene expression data for all the groups were normalized using median normalization method.
cDNA data were normalized using the median normalization method and Affymetrix.CEL file data were processed using the Robust Multi-Array Average (RMA) algorithm [31].
MicroRNA arrays were analyzed using GeneSpring GX v11.5 (Agilent Technologies, CA, USA) with the standard normalization method for one-channel microarrays with the percentile median normalization method.
For an individual class, the median normalization method was generally better than the control normalization method with the exception of the control class whose prediction accuracy was higher using control normalization than median normalization.
We used Median Normalization Method to obtain "Normalized Data," Normalized Data = (Foreground − Background)/median; the median was 50percentt quantile of microRNA intensity which was larger than 30 in all samples after background correction.
In fact, we show that median normalization method or loess normalization based on all genes spotted are unable to cope with situations in which only up-regulated genes are present on the array.
Besides using the default global median normalization method, we also investigated several other normalization methods for the Applied Biosystems data set, including Quantile normalization [ 23], scale normalization [ 24] and Variance Stabilization Normalization (VSN) [ 25], using the limma and vsn packages of R/Bioconductor [ 29].
In addition, to exclude the possibility that our results were influenced by the normalization method, we repeated all the analyses with a different method, i.e. the median normalization method in which each sample was centred based on the median value of all the 147 expressed miRNAs.
It has the following form: (2) d j U Q = U Q K g j ∑ g = 1 G K g j, where UQ X) is the upper quartile of sample X, which is jth sample with normalized counts and K gj > 0. Anders and Huber [ 13] suggested the Median normalization method implemented in the DESeq Bioconductor package.
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