Additional file 2 of Analysis of transcriptomic features reveals molecular endotypes of SLE with clinical implications
Creators: name: Hubbard, Erika L.
name: Bachali, Prathyusha
name: Kingsmore, Kathryn M.
name: He, Yisha
name: Catalina, Michelle D.
name: Grammer, Amrie C.
name: Lipsky, Peter E.
Publisher: name: figshare
Year: 2024
Subjects: subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: Molecular Biology
subject: Ecology
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified

description: Additional file 2. This additional file consists of the following supplemental figures referenced throughout the manuscript: Figure S1. Determination of k clusters. Figure S2. Experimental design of the process used to determine the final 32 modules for endotyping. Figure S3. Machine learning algorithms can predict lupus endotype membership with high accuracy. Figure S4. K-means clustering and comparison of lupus and control samples in GSE88884. Figure S5. K-means clustering of lupus and control samples. Figure S6. Eight molecular subsets in five formative SLE datasets. Figure S7. Modular immune dysregulation. Figure S8. Centroid based ML design to validate the classification of patients into final endotypes. Figure S9. Cosine similarity between A-H endotype designation and 8 clusters identified by k-means of the training/validation or testing cohorts. Figure S10. ML algorithms can predict lupus endotype membership with high accuracy. Figure S11. Unsupervised ML design to validate the classification of patients into final endotypes. Figure S12. All 26 features are required for endotype identification. Figure S13. ML-Predicted endotypes in male SLE patients. Figure S14. One-vs-rest multi-class classification of lupus endotype memberships. Figure S15. Summary Plot of SHAP values for XGB multi-class ML model. Figure S16. Distinguishment of endotype B from A. Figure S17. Distinguishment of endotype C from A. Figure S18. Distinguishment of endotype D from A. Figure S19. Distinguishment of endotype E from A. Figure S20. Distinguishment of endotype F from A. Figure S21. Distinguishment of endotype G from A. Figure S22. Distinguishment of endotype H from A. Figure S23. Gini index analysis reveals features most distinctive of transcriptional perturbations in the seven abnormal lupus endotypes. Figure S24. Lupus subsets derived from clinical features. Figure S25. Machine learning prediction of molecular endotype memberships using clinical metadata as features. Figure S26. Endotype reassignment in GSE88884. Figure S27. Clinical characterization of SLE endotypes. Figure S28. SLEDAI manifestations among six adult lupus endotypes in GSE88884. Figure S29. Organ system involvement among six adult lupus endotypes in GSE88884. Figure S30. Clinical characteristics of five pediatric lupus endotypes. Figure S31. Endotypes of EA adult SLE patients. Figure S32. Endotypes of AA adult SLE patients. Figure S33. Endotypes of NAA adult SLE patients. Figure S34. Distribution of patients in GSE88884 drug treatment cohorts. Figure S35. Endotypes in GSE88884 ILL-2. Figure S36. Clinical characterization of SLE endotypes in GSE88884 ILL-2.