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BMI was standardized to z-scores on the full sample to make gender differences in either means or overall variances clearly visible.
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Surprisingly, PC1 that explained 40.7% of the experimental variance clearly separated the datasets into two groups representing general accession differences.
The 1st component (PC1, 52.32% variance) clearly separated harvest from cold-treated samples.
The second component (7.73% of variance) clearly distinguished three physician-related items from all other items with loadings of 0.91-0.95.
The first two components, which account for a substantial fraction (71.2%) of the total variance, clearly identified four main groups.
The first principal component (which captures 19.06 % of the total variance) clearly subdivides the Caltech samples into those analyzed by single- and paired-end sequencing.
The first Eigenvector (explaining 12.3% of the total variance) clearly separates the two more ancient events/populations (blue and red) from the more recent contributions (green and yellow).
The first PC, explaining 9.97% of the total variance, clearly separated the tetraploid sativa, diploid caerulea, diploid falcata, and tetraploid falcata genotypes.
Such interactions also complicate the statistical modelling of gene-environment interactions since they violate the assumption of independence: with some of the genetic variance clearly depending on exposures.
We exploit the well-known fact that Poisson distributions are well approximated by normal distributions with the same mean and variance, clearly distinguishable by the Poisson distribution property that the mean equals the variance.
Even then, we showed that the biological variance clearly exceeds the technical variance, also in different types of samples, that is, after different treatments of animals, with different gender and at different points of time.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com