The GO Term Finder calculated the p-values that reflect the probability of observing the co-occurrence of proteins with a given GO term in a certain pathway by chance based on a binomial distribution.
By repeating this process 1,000 times and computing the number of common pathways similar to those detected in our analysis of the Flovent dataset, we estimated the probability of observing common pathways by chance.
Finally, we believe that, with the level of rigorousness of the clinical and statistical criteria employed in defining treatment responders and significantly altered genes and pathways, the chance that our findings are falsely positive is minimal.
To assess the likelihood of identifying gene lists that appeared to be enriched for molecular pathways by chance alone, we performed permutation analyses as described in the Supplementary Methods.
This process was repeated N = 1000 times and the probability of a pathway appearing by chance was computed as the number of times it appeared as common in the simulated datasets divided by the number of simulations (P = n / N ).
Crucial to that are the pathways and chances for the infection to be transmitted from the primary outbreak to the rest of the population.
By accurately mapping changes in the genome and subsequent biological/molecular pathways, the chances of finding potential novel treatment targets as well as intervention strategies are enhanced, and ultimately lives can be saved.
The p-value here stands for EASE score, a modified Fisher exact p-value, which reflects the probability that the uploaded list is associated with specific KEGG pathway by random chance.
In this case, pathways would be identified by showing that the number of significant SNPs/genes occurring in a particular pathway is above chance expectation.
Second, Fischer's exact test was used to calculate a P-value to determine the probability that an association between a given gene list and a particular canonical pathway occurred by chance alone.
