Extended Data Fig. 4: InflaMix reliably estimates cluster assignment probabilities.

InflaMix performance in consistent cluster assignment was benchmarked against mixture model variants derived from bootstrapped populations from the (a-c) derivation cohort (Cohort I) and an (d-f) independent Cluster Assignment Validation (CAV) cohort of patients with NHL. Calibrations by linear regression fits of inflammatory cluster assignment (ICA) probabilities by InflaMix to averaged ICA probabilities conferred by all mixture model variants for a given patient are plotted when InflaMix predictions are made with (a, d) no missing laboratory values, (b, e) up to 7 missing laboratory values (simulated over multiple iterations), and (c, f) only the limited 6-lab InflaMix panel (albumin, Hgb, AST, alkaline phosphatase, LDH, and CRP). 95% confidence intervals are provided for the regression fits. The black lines represent ideal calibration, and the gray shaded boxes overlap with concordant cluster assignments. Both Pearson correlation coefficients and Lin’s CCC^31,32 are reported. Intercepts and slopes for the least squares regression line fits, as well as the adjusted Rand indices⁵⁵ for cluster assignment agreement are provided in Supplementary Table 1. AST, aspartate aminotransferase; CCC, Concordance correlation coefficient; CRP, C-reactive protein; Hgb, hemoglobin; LDH, lactate dehydrogenase; NHL, non-Hodgkin lymphoma; PCC, Pearson correlation coefficient.