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The loadings indicate that variable's contribution to the data variability along each PC.
The three most important variables (Table 1) explained an average of 69% of data variability.
These first two components represent the majority (2/3) of the data variability.
They are composed of large volumes of data, high speed data production and information with a high data variability.
Unlike linear regression techniques, GEP does not overpredict mean values and thereby preserves original data variability.
Differences among microbial communities with depth accounted for 4.0% and 14.3% of the overall bacterial and fungal data variability, respectively (Table S1).
With regards to the H1N1 strains, novel laboratory techniques would be required to reduce the data variability so as to eliminate such correlations between parameters.
When calculating multifunctionality based on threshold models, they explained on average 67%30%0% threshold), 54% (60% threshold), and 49%90%0% threshold) of the data variability.
These models capture the data variability reasonably well (e.g., around 70% of the variability).
High data variability was found for samples collected furthest from the dye injection point.
Moreover, it allows the performance of rat testing with minimal constraint, which reduces data variability.
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