Given a non-significant interaction term, we then conducted a two-way ANCOVA to assess whether there was a significant effect of population.
For each GO term, we then generated an empirical distribution for the number of member genes targeted by 39 randomly selected miRNAs.
For each term, we then computed a p-value using hypergeometric test, to indicate the enrichment of the term in this set of genes as compared to the background set of all genes.
To identify unique definitions for each term, we then collapsed definitions based on each distinct combination of components and component thresholds or categories, with a view to grouping broadly similar definitions.
To maximise the specificity, we did an initial scoping search to develop "exercise referral" and other related synonym terms which we then combined with "primary care" search terms and a controlled trial filter.
In order to determine whether differences in driver gene mutation frequency underlie very long-term survival, we then analyzed 27 additional PDACs obtained from an independent cohort of very long-term survivors using deep targeted sequencing.
To evaluate a longer-term impact, we then performed colony formation assays on cells transfected with control or PLK1 shRNA.
In the longer term, we would then observe a faster rate of adaptation in females than males.
Dropping the constant 1 term, we can then decompose the total variance in log R into its component parts using the well-known relation that the variance of the sum of two random variables is equal to the sum of their variances plus their covariance, v a r (R ) = v a r (β k ) + v a r (S e f f N ) + 2 c o v (β k, S e f f N ).
Given a specific GO type GT (BP or MF), for each gene we measured the semantic similarity between its GO terms in A and its GO terms in B. We then took the average over all genes as the similarity of A and B in GT (see Methods for details).
