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We selected this experiment specifically, because treatment and array effects are not partially confounded (as is the case in the cell line dataset), and biological variance is absent.
The gene effects α g 's range uniformly between 0 and 10, and the array effects are linear functions of the gene effects.
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> -wrap-foot> The data were simulated according to the above protocol where only the biological group differences and intensity-dependent array effects were included.
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].
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