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So we used Bonferroni correction to control the false positive rate, as it has been shown to be powerful when applied to the branch-site test [ 36].
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The results suggest that the proposed systematic method is powerful when applied to genome-wide association studies.
Bonferroni's procedure was used to control the family-wise error rate (≤ 0.5%) and correct for multiple testing, as it has been shown to be powerful when the branch-site test is applied without a priori hypothesis to multiple branches on a tree [ 70].
The integrated analysis method has proved to be very useful in previous studies with relatively limited numbers of samples, but was expected to be more powerful when applied to large matched miRNA-gene expression datasets, as in this study (i.e., with limited dimensionality curse, imbalance between the number of estimated genes, and hybridizations to different samples).
Concerning the relative interest of the two tests, the Rν test is revealed to be more powerful when applied to our simulated data set, so we advocate its use.
This suggests that cognitive-behavioural interventions may be more powerful when applied earlier in the illness course before strong associative links to bipolar emotional states are established.
Consequently, this approach may not be very powerful when applied to microsatellite candidates or multiple tightly linked candidates.
It is important to never provide counts that were pre-normalized for sequencing depth/library size, as the statistical model is most powerful when applied to raw counts, and is designed to account for library size differences internally.
This approach is very powerful when applied to long read data with a high error rate, but can be slower and less accurate for short low-error mappings [ 21].
PCA is particularly powerful when applied to so-called "raw" data sets, in which a complete mass spectrum is collected for every pixel in the analysis area.
The overall conclusion is therefore that phase field modelling is distinctly more powerful when applied in 3D space.
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