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We observed a very strong linear correlation between real and in silico reconstructed ratios (Additional file 3), and the ANOVA-derived array effect was very small compared to the treatment effect.
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After applying our proposed filter in the axial measurement of the parametric loudspeaker, a clear frequency dependence from parametric array effect is detected, which exhibits a good match with the well-known theory described by the Gaussian-beam expansion technique.
It should be noted that we do not state that the array effect is absent: our analyses in fact show that an array effect is present in modern dual color microarray experiment.
For dual-color arrays specifically, an array effect is included in the model to determine the technical noise introduced by any between-array differences.
Thus, even though dropping the array effect is beneficial for roughly 2 out of every 3 genes, this percentage is lower than in the other two data sets.
A possible argument for the inclusion of the array effect is the potential competition for spot binding between the co-hybridized samples.
Indeed, the results from the model selection experiments clearly indicate that the model without array effect is the preferred model for both datasets.
A BIC analysis revealed that the model without array effect is preferable for 71% of the genes in the MAQC dataset.
Since not all treatments are observed within the same array, the spot-specific array effect is corrected as a random effect to the model (1) and estimated by REML [ 36].
When intensities instead of ratios were taken into the statistical analysis, no differential genes were detected at a FDR of 10% unless dye and array effect were included in the model.
In all cases the power curve for the analysis including the array effect is steeper, confirming our expectations that when sample size increases, the loss of degrees of freedom is less harmful.
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