A PCA defines differentially Selleckchem TPX-0005 expressed HB components—i.e., orthogonal principal components (PCs). Network analyses and phenotype correlation

tests were then carried out using these PCs as independent variables. To test the robustness of the PCA results, we repeated the PCA using non-overlapping subsets of isolates. Modeling genotype-phenotype associations Phenotype correlation tests consisted of multiple linear and logistic regression models, similar to the tests performed in [10], however in our case we substituted the expression rates of classic var types for HB expression rates, or PCs of HB expression rate profiles. BIC, AIC, R2 and Adjusted R2 were all used to compare the quality of alternative models. Where indicated, host age was included as an independent variable even where it did not appear to have a significant effect in order to eliminate

the potential for observing spurious correlations resulting from co-correlation with this variable, since many weak correlations between disease phenotype and host age have been reported previously (e.g., [27]). Variable selection to optimize models of rosetting To select a set of independent variables that produce the most informative model of rosetting, we started with many possible independent LBH589 variables in a multiple linear regression model, and then successively removed the least significant contributing variable, excluding host age, until the BIC stopped decreasing. We then verified that the BIC increased with the removal of any of the final independent genetic variables. The BIC, AIC, R2 and adjusted R2 scores for the final models after removing host age were also evaluated. Most variable selection procedures were also carried out under the scenario where host age is removed as soon as it is the least significant contributing variable,

and in all cases examined this had no influence on the variable Gefitinib nmr selection results. Identifying rosetting associated HBs or PCs Warimwe et al. test whether particular expression rates can significantly reduce the explanatory power of rosetting on RD as a means to identify a group of var genes that associate with rosetting and RD as opposed to impaired consciousness [10]. However, we reason that even a perfect genetic marker may not substantially reduce the effect of the rosetting coefficient. If there is a BAY 11-7082 tighter relationship between rosetting and RD than between the expression rate of the responsible gene and RD (which is likely the case if the path from gene to rosetting to RD accumulates noise along the way), then the most informative regression model will still primarily depend on rosetting as the primary independent variable. For this reason we take a different approach. We attempt to identify rosetting-specific var/HB expression rates or PCs by considering which var/HB expression rates or PCs remain as independent predictive variables in a model of rosetting after the variable selection procedure described above.