The amount of variation among the isolates that is explained IGF-1R inhibitor by each of the PCs is shown on the right. (B) The PCA

of HB expression rate profiles reflects the differentially expressed HB components, and the first PC defines the extent to which there is a bias toward the expression of var tags with 2 cysteines (cys2). The cys2 expression bias maps roughly to an association with mild versus severe disease spectrum phenotypes. (C) PC1 (and Cys2 var gene expression) correlates with the expression of several HBs, including HB 60. (D) PC1 (and Cys2 var gene expression) anti-correlates with the expression of several HBs, including HB 36. (E) The network of significant correlations between HB expression rate profile principal components (PCs) and disease phenotypes (p ≤ 0.05). SMA = severe malarial anemia, Rosett = rosetting, RD = respiratory Pevonedistat order distress, Severe = severe disease, Mild = mild disease, Older = high host age, Younger = low host age, Par = parasitemia,

BGlu = blood glucose (low levels indicate hypoglycemia), BaseE = base excess (low levels indicate metabolic acidosis), AB = antibody response. We address whether the PCs provide additional information about rosetting beyond what can be predicted based on the expression rates of the classic var types. We start with a multiple regression model of rosetting that has the seven classic var types, plus host age, as independent variables. We then add each of the PCs, one at a time, selleck compound Methocarbamol and observe whether they make a significant contribution to predicting rosetting and/or reduce the BIC of the model. The only PC that is significantly predictive about rosetting in the context of this already over-parameterized model is PC 3, which shows a positive association with rosetting. PC 3 is also the only PC to reduce the BIC (from 50.72 down to 48.36), and it also reduces the AIC (from 21.97 down to 16.73) and increases the adjusted

R2 (from 0.348 to 0.378) (Additional file 3: Table S2). The above findings suggest that, regarding the rosetting pattern, PC 3 provides qualitatively different information from any of the classic var types. PC 3 is dominated by a strong negative value in the dimension of HB 204 expression rate (Figure  5A), which is consistent with PC 3 having a positive association with rosetting, since we established above that HB 204 significantly anti-correlates with rosetting. Next we perform a variable selection procedure to address whether an optimized model of rosetting will contain PCs or classic var types, or both. We start with a multiple regression model of rosetting that includes all 29 PCs and all seven classic var types, and host age, as the independent variables.