Figure 2.

Classification performance of machine learning model on scRNA-seq islet data

(A) A schematic workflow of XGBoost and performance. The machine-learning-based XGBoost model was built for gene selection and classification. The dotted lines show the training and testing procedures, where T denotes the gradient boosting tree models. The double lines show 100 repetitions of the entire workflow.

(B) Boxplots depicting a pairwise comparison of the XGBoost method across all cells (unannotated) in the dataset.

(C) Performance of XGBoost across major cell types for T1D vs. CTL comparison using boxplots.

(D) Performance of XGBoost across major cell types for T1D vs. AAb+ comparison using boxplots.

(E) Performance of XGBoost across major cell types for AAb+ vs. CTL comparison using boxplots.