Classifier training

1. Supplementary Figure 1 DIGE analysis Cluster analysis ELISA assays Prediction analysis (LDA) 1 3 2 Predictor discovery in training set Predictor test in testing set DIGE raw gel images Matched samples SJIA (10 F, 10 Q) POLY (5 F, 5 Q) 2d hierarchical clustering heatmap plotting 12 ELISA Assay development 4 5 7 Test to anticipate SJIA F in Q randomization Training set SJIA (12 F, 12 Q) Testing set SJIA (10 F, 10 Q) ELISA assay FI (27 ) KD (10) SJIA F(12) FI (15) 9 FDR analysis ELISA assay Q->F (5) Q->Q (10) Feature selection Spot finding spot alignment feature extraction 15 protein 2d clustering LDA Fisher exact test Normalization manual review Classifier training Blind testing Classifier training 7 ELISA assay Clustering Box-and-Whisker Analysis Fisher exact test Fisher exact test Manual review Discriminate SJIA F SJIA Q POLY F POLY Q Discriminate SJIA F KD FI Classify SJIA F vsQ Classify SJIA F vsQ Classify SJIA F vs FI MSMS Protein ID

2. Supplementary Figure 2 A B Patient 1 and Patient 2 whole plasma 3 2 4 1 5 Removed proteins: 1 anti-trypsin 2 albumin 3 serrotransferrin 4 Ig heavy chain 5 Haptoglobin 6 Ig light chain 6 MW/1000 Patient 1 and Patient 2 after protein depletion Hemoglobin pH

3. SJIA F Q C3 AGP1 ACT SAP SAA S100A8 S100A9 HP CRP C9 C4 APO-IV APO-L1 AMBP KLKB1 VDB CFH ATIII A2M ATIII APO-E APO-D FGG FGB TTR GSN APOA1 Figure 1

4. POLY F Q SJIA SJIA SJIA A B F Q F KD F FI 0.30 0.25 TTR CFH 0.20 APO A1 A2M GSN False discovery rate (FDR) 0.15 C4 AGP1 ACT 0.10 APO IV SAP HP 0.05 CRP S100A8 S100A9 0.00 SAA 20 1 2 5 10 Number of features called significant P value: 1.1 E -4 P value: 0.5 P value: 0.19 P value: 1.4 E -4 Figure 2

5. Goodness of class separation – D probability SJIA Training SJIA Test Feature# 4 5 6 7 8 9 12 4 5 6 7 8 9 12 Figure 3