Additional file 34 of Seven-chain adaptive immune receptor repertoire analysis in rheumatoid arthritis reveals novel features associated with disease and clinically relevant phenotypes

Additional file 34: Table S27. Predictive performance of the multi-chain AIRR predictors evaluated in the present study. The predictive performance of each multi-chain AIRR predictor is determined using the accuracy, sensitivity, specificity, precision and false positive rate measures. For each multi-chain AIRR predictor, the number of individuals that were properly classified is also shown. The AIRR feature column indicates if the multi-chain AIRR predictor was built using information from a single AIRR- feature or aggregating information from different AIRR features. Information from different AIRR features was aggregated according to different thresholds of accuracy when the sample size of the classes tended to be balanced (i.e., predictor of rheumatoid arthritis diagnosis and disease activity). The aggregation of information from different AIRR features was based on different specificity thresholds when the clinical interest was to identify the minor class (i.e., predictors of TNFi response, ACPA and RF phenotypes). The predictive performance of redundant multi-chain AIRR predictors obtained after applying different accuracy or specificity thresholds is not shown.

补充材料34：表S27。本研究中评估的多链适应性免疫受体库（AIRR）预测模型的预测性能。各多链AIRR预测模型的预测性能以准确率、灵敏度、特异度、精确率及假阳性率作为评估指标。同时列出各多链AIRR预测模型的正确分类个体数。AIRR特征列用于说明该多链AIRR预测模型是基于单一AIRR特征构建，还是整合了多个不同AIRR特征的信息。当各类别样本量趋于均衡时（如类风湿关节炎诊断及疾病活动度预测模型），不同AIRR特征的信息整合将以准确率作为不同阈值的依据。当临床需求为识别少数类样本时（如抗肿瘤坏死因子抑制剂（TNFi）治疗应答、抗环瓜氨酸肽抗体（ACPA）及类风湿因子（RF）表型预测模型），不同AIRR特征的信息整合将以特异度作为不同阈值的依据。经不同准确率或特异度阈值筛选后得到的冗余多链AIRR预测模型，其预测性能未在本表中展示。