LASSO and PCA
To determine the minimal number of antibody features that distinguish between different groups, a least absolute shrinkage and selection operator (LASSO) feature reduction method was employed as previously described37. Cross validation was performed iteratively (repeated 1,000 times; 10-fold cross validation) to identify the optimal value of the regularized parameters. Unsupervised principal component analysis (PCA) was then performed on LASSO-selected antibody features (which resolves multiple variables into principal components that describe the variance within the data set). The contribution of each variable in describing the variance within each principal component is represented on loading plots.