Exact(36)
All normalization calculations were performed using medians to multiplicatively normalize data.
Figure 9: In the Platinum Spike dataset, all normalization methods resulted in similar detection of differential gene expression, with MedianCD and SVCD normalization being only marginally better.
SUV also performed well for all normalization factors investigated (SUVBW R 2 = 0.89, SUVBSA R 2 = 0.91, SUVLBM R 2 = 0.82, SUVBMI R 2 = 0.88).
The weight (score) of the sample s fi at time t, ω fi t, is obtained by the sum of all normalization function values as: ω fi t = ϕ ̄ p ( s fi t ) + ϕ ̄ g ( s fi t ) + ϕ ̄ h ( s fi t ) + ϕ ̄ e ( s fi t ) + ϕ ̄ px ( s fi t ) + ϕ ̄ py ( s fi t ) (8).
All normalization and permutation analyses were carried out using customized R-scripts which can be found at http://naturalvariation.org/aquilegia.org/aquilegia
All normalization and differential expression analysis was conducted using the limma software package[19] for the R programming environment (http://www.r-project.org).org
Similar(24)
Again, this 'winner-take-all' normalization is a feature of both human EEG and vertebrate single unit experiments (25, 30).
From all the normalization methods tested, vsn was most robust, whereas the performance of alternative normalization algorithms was more platform-dependent.
All the normalization procedures were performed using R Bioconductor packages SCAN.UPC 54 and affy 55.
All these normalization models, as it turns out, have similar behavior.
Not all the normalization techniques are equally suited for the different match score distributions.
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