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Even when the un-normalized data were removed from the comparison, the agreement among normalization methods was as low as 10%% (i.e., compare the number of overlapping genes in Table 1 with the Med-normalized number for Enviroment × Sex).
A thorough comparison among normalization methods has been published [ 21].
There are more differences among normalization methods than background correction methods.
Both Figures 2 and 3 show that there are more differences among normalization methods than among background correction methods.
Our comparison results show that even though some background correction methods are slightly better than others, the differences are much smaller than the differences among normalization methods.
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Brauers and Zavadskas (2006) proved that this ratio is the most robust selection among different normalization equations for the MULTIMOORA method.
Since the number of absorbed cells may vary among plates, normalization is required.
Among four normalization methods, the result of no normalization performs the worst in that both statistical measurement scores are the smallest in both data sets.
Plots B and D in both Figures 2 and 3 show that the difference between different background correction methods is not very much, their difference is much smaller than the differences among four normalization methods.
Marginal difference was observed in outcomes among different normalization methods, including the intra-platform reproducibility [see Additional file 5] and fold change correlation with TaqMan assays [see Additional file 6].
To compare the number of DE genes and the number of common DE genes found among the normalization methods performed for a particular data set, we generate balloon plots and Figure S5) and Venn diagrams (see Figure S4).
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