Gene expression
The PCA of gene expression log-fold changes revealed no obvious
genome-wide parallelism or antagonism: just like with genetic divergence
data, all principal components were similar in magnitude, and the shape
of the gene cloud did not have any structure suggesting some predominant
direction of overall gene expression change (Figure 5).
Figure 5. Principal component analysis of gene expression data
(log-fold changes). Points are individual genes; vectors are original
contrasts. Bar chart displays the proportion of variance explained by
each principal component.
However, the gene set overlap analysis (Figure 6) revealed that all
contrast-specific gene sets were significantly smaller than expected,
while nearly all gene sets shared between three or more studies were
significantly larger than expected. Interestingly, the two cases of
adaptation to high CO2 did not show more overlap between
each other than expected by chance, while sharing a higher-than-expected
number of outlier genes with other datasets.
Figure 6. Gene expression gene set overlap analysis for the top
12.5% (a) and bottom 12.5% (b) quantiles of log-fold-change. Bar chart
displays the number of outlier genes shared between contrasts identified
by connected dots below; the first five bars are numbers of
contrast-specific outlier genes. Asterisks above bars indicate gene sets
that are significantly larger (red) or smaller (blue) than expected.