Immune predictors of oral poliovirus vaccine immunogenicity among infants in South India

Figures

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  Fig. 1. Flow chart of infants included in the analysis. P3 poliovirus serotype 3, mOPV3 monovalent serotype 3 oral poliovirus vaccine, PBMC peripheral blood mononuclear cells, EE environmental enteropathy, CRP C-reactive protein.

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  Fig. 2. Correlation and clustering analysis of infant immune status variables. a Pearson’s correlation coefficient for the 51 variables in each of the 4 measurement “modules”. Pairwise comparisons are based on all available data (n = 292 infants for data on biomarkers of environmental enteropathy (EE), plasma cytokines and CRP, and leukocyte counts; and n = 129 infants for data on ex vivo flow cytometry). b Heatmap for each infant with variables (rows) clustered using Ward’s minimum variance hierarchical clustering method and infants (columns) grouped by treatment arm and OPV seroconversion status. A tree with 6 clusters showing the grouping of the variables is shown on the left of the heatmap and the variables included in each group on the right. In both plots the variables were log-transformed, normalized, and truncated at +/−3 standard deviations before analysis and plotting. Color bars indicate the scale for each plot. Only infants with complete data for all variables were included and invariant variables after normalisation and truncation were removed (126 infants, 37 variables). SN seroconversion negative, SP seroconversion positive, Az azithromycin arm, Pl placebo arm.

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  Fig. 3. Random forests analysis to predict seroconversion and study arm. The accuracy of random forests analysis to predict a seroconversion and b study arm for each measurement module individually and all modules combined. For each analysis we performed a 10-fold cross-validation repeated 20 times. The boxes correspond to the interquartile range for the accuracies in the prediction set, with the solid line showing the median and the whiskers extending to the 10th and 90th percentile. The dashed line indicates expected accuracy if a random choice were made. The top eight most important variables that predict c seroconversion and d study arm in one best fit full random forests model are shown as correlation networks. The color of the circle around each variable indicate whether they were positively (green) or negatively (red) associated with seroconversion or azithromycin respectively. The size of each network node indicates the strength of correlation with the outcome and the color indicates whether the variable describes pathogens in stool (orange), EE biomarkers (blue) ex vivo T-cell and total leukocyte count data (pink), or plasma cytokines (green) (as for a and b). Demographic…