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The eigenvector with the highest eigenvalue, which corresponds to the highest variance, is the first principal component of the data. .
The maximum variance across the whole region is 0.053 in Figure 9c and 0.10 in Figure 9d, and the highest variance is observed where there is a lack of discrimination, i.e. where bel{A}≈bel{B}.
For the other CER species the highest variance is caused by the incident reactions.
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Here, the features with the highest variance are kept to do the reduction.
As a next step the m principal components with the highest variance are selected, resulting in a p×m transformation matrix W and a matrix P=D·W that represents the m principal components.
Since the highest variance was associated with Cp+/-genotype, we removed this group from the statistical analysis to avoid misrepresentation and substantial reduction of power.
Usually, principal components with the highest variance are selected in three steps.
To facilitate interpretability of the clustering, the 250 genes with the highest variance were selected from the data set.
The 5% of windows with the highest variance were considered to have QTL and defined as type 3 loci.
If multiple probe sets represented the same gene and they showed same direction of expression, the probe set with the highest variance was used.
When multiple probes match the same Entrez id (and had the same extension), the one with the highest variance was used.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com