The similarity in cluster results shared by the different clustering methods confirmed the scientific rigor of this analysis (Additional file 5: Table S4).
Because the test statistic will be driven by the different clustering of the variant locations in the case and control groups, the power of the test statistic is not negatively influenced by the presence of different effect directions in the region, i.e. deleterious alleles and protective alleles.
By direct comparison of the different clusters, the predicted proteins shared by both species could be identified.
Finally, to compare the level of diversity within clusters, estimates of Â, H O and H E were calculated for the different clusters determined by STRUCTURE analyses by pooling all populations from each cluster.
We ensured of the good representativeness of each development style in the different clusters, by computing and analyzing the index of qualitative variance (IQV) of each cluster.
To answer the second research question, the IDB Analyzer 3.1 was used to compare the means in the student achievement (plausible values) between the different clusters by country.
First, a supervised analysis was done by testing the enrichment of the different clusters in the targets of several well established RBP, such as Pub1p, Nlp3p, Puf3p, or Puf4p [22], [22], using a binomial test.
Although variability in the numbers of families represented by differentially-expressed genes in the different clusters was not large, there was a general trend toward higher diversity with increasing differential gene expression.
By analyzing the genes of the different clusters and their expression profiles, we were able to reconstruct three main phases of osteogenic development: proliferation, matrix maturation, and mineralization.
The details of the different clusters created by hierarchical clustering are also explained in detail.
