Principle components analysis shows tightly segregated clustering based on culture condition, not by UV exposure.

<p>(alternate viewing angle and axis values available in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139307#pone.0139307.s002" target="_blank">S2 Fig</a>). PCA is a statistical analysis tool which reduces the dimensionality of data by determining the key variables resulting in differences seen between samples[<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0139307#pone.0139307.ref025" target="_blank">25</a>]. Each axis of this PCA map represents a linear combination of expression levels from many thousands of gene transcripts such that, combined, the maximum variation among all data points is achieved on only three axes. The result gives a visual representation of which samples behave similarly to each other by their physical closeness in three dimensions, while including information from many thousands of variables (gene expression levels). This is a bird’s eye view of the entire set of gene array data, for which absolute units and values can be considered arbitrary. The following in-depth analysis of pathway enrichment and specific gene expression provide insight into how each group differs from the others in their gene expression.