1. A prospectively validated csEN model accurately predicts dynamic changes of the maternal immune system over the course of pregnancy.
A prospectively validated csEN model accurately predicts dynamic changes of the maternal immune system over the course of pregnancy. (A) Correlation network revealing the relationships between immune features within and across mass cytometry data categories (Spearman’s coefficient). (B) Cell signaling–based penalization matrix that allowed prioritizing canonical, receptor-specific signaling responses (see Materials and Methods). Signaling responses amenable to prioritizing are highlighted in blue (endogenous), yellow (IFN-α), red (IL), or green (LPS). (C) Cross-validated csEN model predicting gestational age at time of sampling. Red/blue dots highlight model components that trended upward/downward during pregnancy. Dot size indicates the correlation between model component and gestational age (Spearman’s coefficient). (D and E) Training cohort. (D) csEN model prediction of the gestational age at time of sampling (R = 0.89, P = 2.2 × 10−16, n = 18, cross-validation). (E) Line plots depicting csEN model values for each patient during pregnancy and for the postpartum samples. Red lines and red shadow represent median and 95% confidence interval of EN components. (F and G) Validation cohort. (F) csEN model prediction of the gestational age at time of sampling (R = 0.62, P = 2.4 × 10−4, n = 10, Spearman’s coefficient). (G) Line plots depicting csEN model values for each patient (validation cohort).
Nima Aghaeepour et al. Sci. Immunol. 2017;2:eaan2946
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