Additional file 1 of Cyclic multiplex fluorescent immunohistochemistry and machine learning reveal distinct states of astrocytes and microglia in normal aging and Alzheimer’s disease

Additional File 1: Table S1. Demographic and neuropathological characteristics of study subjects. Description: Abbreviations: ADNC = AD neuropathological changes; APOE = Apolipoprotein E genotype; CAA = cerebral amyloid angiopathy; CVD = cerebrovascular disease; F = female; LBD = Lewy body disease; M = male; NA = Not available/applicable; NOS = not otherwise specified; NP Dx = neuropathological diagnosis. Table S2. Primary and secondary antibodies used in this study and sequence of immunohistochemistry cycles. Description: Note: GFAP and DAPI detection are needed in all the cycles to guarantee an adequate alignment of the images. Abbreviations: AF488 = AlexaFluor 488; Cy = cyanine; Dk = donkey; Gt = goat; Ms = mouse; Rb = rabbit. All secondary antibodies were purchased from Jackson ImmunoResearch Labs, West Grove, PA. Table S3. Results of mixed effects regression models. Description: Results of mixed effects regression models with diagnosis (CTRL vs. AD) or state (homeostatic vs. intermediate vs. reactive) as a fixed effect, respectively, and subject ID as random effect in both cases, are reported. Table S4. Model performance statistics for CTRL vs. AD binary classifiers. Description: Model performance statistics for the binary classification task of CTRL vs. AD for both the gradient boosting machine (GBM) and the convolutional neural network (CNN) machine learning models are reported. For all heuristics except for AUC and AUCPR (which are not threshold-dependent), the threshold was chosen by maximizing the accuracy. 95% confidence intervals were estimated by bootstrapping the hold-out test set across 500 iterations. Table S5. Results of Bayesian hyperparameter optimization. Description: The final hyperparameters determined by the Optuna hyperparameter tuning framework are reported. The Optuna optimizer maximized the out-of-sample area under the receiver operating characteristic (ROC) curve (AUC), which in turn was determined by 3-fold cross-validation for each trial.