Extended Data Fig. 4: Training a liquid biopsy model using PROMAD.

A. Boxplot of increasing number of ratios that are required to predict acute allograft rejection from liquid biopsy samples. Each point is an evaluation of model performance on an independent dataset. Points that are joined by a line represent the same dataset. Box plots show Q1, median and Q3, and the lower and upper whiskers show Q1 – 1.5 × IQR and Q3 + 1.5 × IQR, respectively. B. Bar plot of model coefficients for our liquid biopsy model. C. Dot plot of liquid biopsy model performance on the AUSCAD cohort with a loess smoothed curve representing the mean trend as the number of training datasets increases. The shaded area around the curve indicates the 95% confidence interval, reflecting the variability around the estimated mean trend. D. A pair of ROC curves comparing model performance on the AUSCAD cohort. Both models are trained across multiple organs, however their integration algorithms differ. TOP is coloured yellow, and ComBat is coloured blue. E. A boxplot of model performance across peripheral blood datasets within PROMAD (n = 23 datasets from 2422 biologically independent patient samples), when tissue weighting is applied to the TOP algorithm. Each point is an evaluation of model performance on an independent dataset. Box plots show Q1, median and Q3, and the lower and upper whiskers show Q1 – 1.5 × IQR and Q3 + 1.5 × IQR, respectively. A 3-way ANOVA using organ, dataset and weighting strategy was performed to assess if weighting strategy impacted AUC. F. Boxplot comparison of model performance across all datasets within PROMAD. We compare three integration algorithms (Combat, Quantile normalization and TOP). We split the model performance by technology to demonstrate TOP’s ability to cross technologies more easily. Box plots show Q1, median and Q3, and the lower and upper whiskers show Q1 – 1.5 × IQR and Q3 + 1.5 × IQR, respectively. A 2-way repeated measures anova was performed to test the impact of integration strategy on AUC.