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Each of the remaining principal components explains less than 10% of the variance in the data.
The transformation of amino acid usage into two principal components explains in total 89% of the variability observed (supplementary fig. S7, Supplementary Material online).
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The first two principal components explain 72.73% of the variance.
The first two principal components explain 36%% of the variance.
The first two principal components explain a majority (61%) of the variance in student responses.
The first three principal components explain 48.09, 14.62 and 8.47% of the total variance, respectively.
We use PCA to characterize those principal components explaining the most variation among the dataset.
The PCA yielded three principal components explaining 78.38 % of the total variance of the 11 parameters.
These ten principal components explained the total variance of the data, and 9 of them were linearly independent.
The PCA results identified, based on the original 12 variables, five principal components explaining 69.3% of total variance (Table 4).
Overall analysis of the data revealed two principal components explaining 73% of the variation in gene expression (Figure 2a).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com