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miR-200b and miR-452 were used to test for the effect of normalization on target genes.
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While boxplots are easy to generate and interpret, we suggest in addition to these a different type of plot for evaluating the effect of normalization, especially for experiments dealing with sample groups.
The two-way Student t test with Bonferroni correction (p-values > 0.05 considered significant) was used for univariate analysis to investigate the effect of normalization of single regional measures (Tables 6; 7).
Hierarchical clustering, bar plots showing expression levels, and MDS in two dimensions were strategies used for assessing data quality and evaluating the effect of normalization (Additional file 3).
To assess the effect of normalization on variation for data in Figure 7 we calculated IQR for M values of identified populations.
By resorting to analyze features in the form of ratios, we attempted to minimize the effect of normalization and look for co-varying signals in each experiment.
The effect of normalization is most pronounced for the comparison pc; depending on the normalization method between 7% and 74% of the genes appear regulated.
We also discuss the effect of normalization (dividing by the number of authors on a paper) on these statistics.
To examine the effect of normalization on the results, quantile normalization, scale normalization (as defined for the MAS 5.0 algorithm) or loess was used in combination with each of the background methods discussed in this paper.
After this segmentation procedure, probability maps of gray matter were "modulated" to account for the effect of spatial normalization by multiplying the probability value of each voxel by its relative volume in native space before and after warping.
We also propose two variants of the DTLZ test problems for examining the effect of the normalization in MOEA/D.
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