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Predictor discovery in training set

Figure 1. DIGE analysis. Cluster analysis. ELISA assays. Prediction analysis (NSC/LDA/ROC). 7 feature biomarker panel. Two class classification. 1. 3. 2. Predictor discovery in training set. Predictor test in testing set. DIGE raw gel images SJIA ( 13 F , 13 Q)

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Predictor discovery in training set

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  1. Figure 1 DIGE analysis Cluster analysis ELISA assays Prediction analysis (NSC/LDA/ROC) 7 feature biomarker panel Two class classification 1 3 2 Predictor discovery in training set Predictor test in testing set DIGE raw gel images SJIA (13 F, 13 Q) POLY (5 F, 5 Q) 2d hierarchical clustering heatmap plotting Literature review + literature candidates Test to anticipate SJIA F in Q Three class classification 4 5 6 7 Training set SJIA (24 F, 14 Q) POLY (15 F, 10 Q) Testing set SJIA (24 F, 14 Q) POLY (15 F, 11 Q) ELISA assay Q->F (16) Q->Q (19) F (3) ELISA assay FI (27 ) KD(10 F) Mann Whitney Spot finding spot alignment feature extraction LDA Predictions P < 10-5 12 ELISA assays 10 protein candidates FI, KD With SJIA F (48) SLE (1) NSF (3) Classifier training NSC class prediction algorithm Normalization manual review ELISA assay SJIA (48 F, 28Q) POLY (30 F, 20 Q) LDA Fisher exact test Predictors of 4 ~ 12 features Clustering Fisher exact test LDA Fisher exact test 889 discrete spot features Ten-fold cross-validation Clustering Fisher exact test LDA/ROC Training set F->Q (12) Q->Q (13) Testing set F->Q (4) Q->Q (6) Discriminate SJIA F SJIA Q POLY F POLY Q Discriminate SJIA F KD FI Manual review Classify SJIA F vs Q POLY F vs Q Training set SAF (36) KD (6) FI (20) Testing set SAF (12) SLE (1) KD (4) FI (7) Analysis of goodness of class separation ROC: separation by ESR or CRP MSMS ID 97 spots

  2. SJIA SJIA F F Q Q SJIA SJIA POLY POLY F F Q Q F F Q Q Figure 2 A B C D SAP A2M APOAIV CFHR1 HP ATIII CRP ATIII HP GSN A2M A2M CFHR1 GSN GSN TTR GSN APOA1 A2M APOA1 ATIII APOA1 ATIII APOA1 TTR APOA1 APOA1 APOA1 APOA1 SAP APOIV APOIV APOA1 HP APOA1 CRP APOA1 HP MRP14 HP HP HP HP HP HP HP HP HP HP MRP8 MRP8 MRP8 MRP8 MRP14 SAA SAA SAA SAA SAA SAA

  3. Figure 3 A B C D Goodness of class separation – D probability POLY Training POLY Testing SJIA Training SJIA Testing Feature# 4 5 7 8 12 4 5 7 8 12 4 5 6 7 8 9 12 4 5 6 7 8 9 12

  4. Figure 4 POLY F vs. Q SJIA F vs. Q SJIA F vs. Q Sensitivity ESR AUC: 0.90 TRAINING AUC: 0.75 TRAINING AUC: 0.84 CRP AUC: 0.73 TESTING AUC: 0.82 TESTING AUC: 0.59 1- Specificity

  5. Figure 5 B A C D E G H Training set n = 38 Testing set n = 38 All data n = 76 Testing set n = 38 Training set n = 25 Testing set n = 10 SJIA SJIA SJIA • Biomarker panel • of 7 members • HP • APO AI • A2M • SAP • CRP • MRP8/MRP14 • SAA SJIA SJIA SJIA Q->F (M2) Q->F (M6) Clinical diagnosis Clinical diagnosis Clinical diagnosis Clinical diagnosis Clinical diagnosis Clinical diagnosis F Q F Q F Q F Q Q->F Q->Q Q->F Q->Q 24 14 24 14 48 28 3 8 8 19 12 13 4 6 n = n = n = n = n = n = LDA Testing LDA Testing LDA Testing Classified as F Classified as F Classified as F Classified as F Classified as Q->F Classified as Q->F 18 5 21 4 43 9 3 4 5 8 8 3 1 2 Classified as Q Classified as Q Classified as Q Classified as Q Classified as Q->Q Classified as Q->Q 6 9 3 10 5 19 0 4 3 11 4 10 3 4 75% 64.3% 87.5% 71.4% 89.6% 67.9% 100% 50% 62.5% 61.1% 66.7% 76.9% 25% 66.7% Percent Agreement with clinical diagnosis + - Percent Agreement with clinical diagnosis + - Percent Agreement with clinical diagnosis + - Percent Agreement with clinical diagnosis Percent Agreement with clinical diagnosis + - Percent Agreement with clinical diagnosis + - 71% 81.5% 81.6% 72% 50% Overall P = 3.6 X 10-2 Overall P = 3.9 X 10-4 Overall P= 3.3 X 10-7 Overall P = 4.7 X 10-2 Overall P = 1

  6. Figure 6 A D B C SJIA F SJIA F NSF KD FI SLE KD FI Data set n = 27 Data set n = 37 48 3 10 27 1 13 12 12 SJIA F NOT-SJIA F SJIA F NOT-SJIA F Clinical diagnosis Clinical diagnosis SAP MRP8/MRP14 51 38 13 24 n = n = SAA HP MRP8 Unsupervised clustering Unsupervised clustering MRP14 CRP HP Classify as SJIA F Clustered as SJIA F 40 0 10 1 A2M Classify as NOT-SJIA F Clustered as NOT-SJIA F 11 38 3 23 APO A1 CRP 78.4% 100% 77% 96% - - Percent Agreement with clinical diagnosis + Percent Agreement with clinical diagnosis + SAA APOA1 87.6% 85% Overall P = 2.0 X 10-15 Overall P = 8.12 X 10-6 SAP A2M

  7. Figure 7 A B C D SJIA F SJIA F KD FI KD FI SLE NSF Data set n = 62 Data set n = 27 FI KD SJIA F FI KD SJIA F SLE NSF 36 6 20 12 4 7 1 3 SJIA F NOT-SJIA F SJIA F NOT-SJIA F Clinical diagnosis Clinical diagnosis 36 15 12 26 n = n = LDA LDA Classify as SJIA F Classify as SJIA F 36 2 13 0 Classify as NOT-SJIA F Classify as NOT-SJIA F 0 24 2 12 100% 92.3% 86.6% 100% - - Percent Agreement with clinical diagnosis + Percent Agreement with clinical diagnosis + 96.8% 92.5% Overall P = 3.35 X 10-15 Overall P = 5.98 X 10-6

  8. QQ QF 7 feature 6 feature 5 feature 19 16

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