Additional file 1 of Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score
Creators: name: Lim, Ashley J. W.
name: Tyniana, C. Tera
name: Lim, Lee Jin
name: Tan, Justina Wei Lynn
name: Koh, Ee Tzun
name: Chong, Samuel S.
name: Khor, Chiea Chuen
name: Leong, Khai Pang
name: Lee, Caroline G.
Publisher: name: figshare
Year: 2024
Subjects: subject: Biochemistry
subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Biotechnology
subject: Ecology
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Information Systems not elsewhere classified
subject: Mathematical Sciences not elsewhere classified

description: Additional file 1: Figure S1–Figure S15. Figure S1. Principal Component Analysis (PCA) of Case and Control samples from WXS and WGS respectively for the evaluation of underlying batch effects. (a) Plot of % of variance explained by each principal component (PC). (b) PCA plot of the first two principal components of variation based on the combined case and control samples from differing data sources. (c) Boxplots of the case and control samples within PC1. (d) Boxplots of the case and control samples within PC2. Figure S2. Predictive performance of 9 selected SNPs in unseen Test Set 1. ROC-AUC curves with F1 score, Accuracy, Sensitivity, and Specificity of 9 selected SNPs using (a) Logistic Regression, (b) Naïve Bayes, (c) Random Forest, (d) XGBoost, and (e) Support Vector Machine (SVM) classifiers. Figure S3. Predictive performance of 9 selected SNPs in unseen Test Set 2. ROC-AUC curves with F1 score, Accuracy, Sensitivity, and Specificity of 9 selected SNPs using (a) Logistic Regression, (b) Naïve Bayes, (c) Random Forest, (d) XGBoost, and (e) Support Vector Machine (SVM) classifiers. Figure S4. Predictive performance of 9 selected SNPs in unseen Test Set 3. ROC-AUC curves with F1 score, Accuracy, Sensitivity, and Specificity of 9 selected SNPs using (a) Logistic Regression, (b) Naïve Bayes, (c) Random Forest, (d) XGBoost, and (e) Support Vector Machine (SVM) classifiers. Figure S5. Predictive performance of 9 selected SNPs in training set using fivefold cross-validation. Precision-Recall curves of 9 selected SNPs using (a) Logistic Regression, (b) Naïve Bayes, (c) Random Forest, (d) XGBoost, and (e) Support Vector Machine (SVM) classifiers. Figure S6. Predictive performance of 9 selected SNPs in unseen Test Set 1. Precision-Recall curves with of 9 selected SNPs using (a) Logistic Regression, (b) Naïve Bayes, (c) Random Forest, (d) XGBoost, and (e) Support Vector Machine (SVM) classifiers. Figure S7. Predictive performance of 9 selected SNPs in unseen Test Set 2. Precision-Recall curves with of 9 selected SNPs using (a) Logistic Regression, (b) Naïve Bayes, (c) Random Forest, (d) XGBoost, and (e) Support Vector Machine (SVM) classifiers. Figure S8. Predictive performance of 9 selected SNPs in unseen Test Set 3. Precision-Recall curves with of 9 selected SNPs using (a) Logistic Regression, (b) Naïve Bayes, (c) Random Forest, (d) XGBoost, and (e) Support Vector Machine (SVM) classifiers. Figure S9. Predictive performance of calculated PRS in unseen Test Set 1. ROC-AUC curves with F1 score, Accuracy, Sensitivity, and Specificity of PRS using (a) Logistic Regression, (b) Naïve Bayes, (c) Random Forest, (d) XGBoost, and (e) Support Vector Machine (SVM) classifiers. Figure S10. Predictive performance of calculated PRS in unseen Test Set 2. ROC-AUC curves with F1 score, Accuracy, Sensitivity, and Specificity of PRS using (a) Logistic Regression, (b) Naïve Bayes, (c) Random Forest, (d) XGBoost, and (e) Support Vector Machine (SVM) classifiers. Figure S11. Predictive performance of calculated PRS in unseen Test Set 3. ROC-AUC curves with F1 score, Accuracy, Sensitivity, and Specificity of PRS using (a) Logistic Regression, (b) Naïve Bayes, (c) Random Forest, (d) XGBoost, and (e) Support Vector Machine (SVM) classifiers. Figure S12. Predictive performance of calculated PRS in the training set using fivefold cross-validation. Precision-Recall curves of PRS using (a) Logistic Regression, (b) Naïve Bayes, (c) Random Forest, (d) XGBoost, and (e) Support Vector Machine (SVM) classifiers. Figure S13. Predictive performance of calculated PRS in unseen Test Set 1. Precision-Recall curves of PRS using (a) Logistic Regression, (b) Naïve Bayes, (c) Random Forest, (d) XGBoost, and (e) Support Vector Machine (SVM) classifiers. Figure S14. Predictive performance of calculated PRS in unseen Test Set 2. Precision-Recall curves of PRS using (a) Logistic Regression, (b) Naïve Bayes, (c) Random Forest, (d) XGBoost, and (e) Support Vector Machine (SVM) classifiers. Figure S15. Predictive performance of calculated PRS in unseen Test Set 3. Precision-Recall curves of PRS using (a) Logistic Regression, (b) Naïve Bayes, (c) Random Forest, (d) XGBoost, and (e) Support Vector Machine (SVM) classifiers.