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Differential gene expression was performed at the level of genes with the EBSeq package [ 94] (version 1.0) using the median normalization approach and 10 iterations of the algorithm used by EBSeq.
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Microarray data were normalized using median normalization method.
Expressed data were normalized using median normalization.
The expression data were normalized using median normalization.
Data were normalized using median normalization across the array.
The second analytical approach involved a simple median normalization of the samples, followed by a t-test (assuming unequal variance) to compare Ct values between Con and AC samples.
Expressed data were normalized using the Median normalization.
Reads were aligned using the TopHat algorithm and expression values summarized using HTSeq17, 18. Raw counts were normalized using Conditional Median normalization.
Data was read into R and normalized within arrays using median normalization and between arrays using Aquantile normalization from the Limma package [56].
Median normalization has previously been used in label-free proteomics approaches [ 17, 18].
Further, we investigated other novel normalization approaches which, to our knowledge, have not been applied to imaging data, such as median normalization or TIC normalization with manual exclusion of mass ranges.
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