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Additionally, no attempt was made to look at non-constant residual variance relationships with bodyweight, or curvature in the regression.
Different methods have been applied to simulated datasets, in which a variety of mean and variance relationships were imposed.
Similar(56)
Mean and variance relationship should be investigated before the application of NPMVS.
In addition, our method exploits mean and variance relationship, which is generally not considered in standard procedure.
Here, we propose NPMVS (Non-Parametric Mean Variance Smoothing), a method to estimate the mean and variance relationship, which is more general and can capture a wider range of non-linear relationships that exist in microarray experiments (Figure 1).
The difference in variance was not due to a mean and variance relationship.
An advantage of imputation is that it maintains the co-variance relationships between all plot attributes, meaning any measurements taken on a plot can also be imputed, even if they play no part in nearest neighbor selection.
The simulation study showed that NPMSV performed better than limma in case 0, and the two methods were competitive in other mean-variance relationships.
The simulation study showed that NPMVS outperformed the other two popular shrinkage estimation methods in some mean-variance relationships; and NPMVS was competitive with the two methods in other relationships.
When model validation indicated normality, but heterogeneity of variances, relationships were defined using linear regression to which a generalised least squares estimation procedure [45] was applied, as detailed in [43].
The normalized read count data were then used to calculate mean-variance relationships and sample weights in voom [ 56].
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