Figure 4.

FI₃₄ score correlates with the abundance of a microbial coabundance module. Genus-level OTUs were filtered for prevalence in at least 25% of subjects and normalized by DESeq2 variance-stabilizing transformation. Correlation coefficients were calculated between all pairs of genera using the Sparse Inverse Covariance Estimation for Ecological Association and Statistical Inference (SPIEC-EASI) algorithm. Positive correlation coefficients were retained for downstream analysis. (A) A microbial coabundance network consisting of positively coabundant genera is plotted. Vertices or points represent a single OTU genus. Edges or lines emanating from nodes connect significantly coabundant genera as inferred by SPIEC-EASI. Relative point positions were plotted according to the competing strengths of their coabundance correlations (Fruchtermann–Reingold projection). Points with similar connectivity were grouped into a module (indicated by color) using Weighted Gene Co-expression Network Analysis (WGCNA). Genera lacking significant coabundance are plotted as empty, white points. Module abundances that correlated with FI₃₄ score or chronological age following correction for clinical and technical confounding (p < .05) and false-discovery rate (FDR; q < 0.05 and q < 0.20) are highlighted in red or blue, respectively. (B) Turquoise and green module abundances (eigenvalue) are plotted against FI₃₄ score or chronological age with respective Spearman rho correlation statistics and p values corrected for clinical and technical confounding. Linear best-fit lines are plotted in blue with shaded 95% confidence intervals. (C) DESeq2-normalized abundances of turquoise and green module OTUs are expressed as heatmaps. OTU abundances are sorted along the x-axis by increasing FI₃₄ score (top panel) or chronological age (bottom panel). Supplementary Figures 6 and 7 contain expanded heatmaps of all modules and member genera sorted by increasing FI₃₄ score or chronological age, respectively.