Additional file 5 of Systematic interrogation of mutation groupings reveals divergent downstream expression programs within key cancer genes

Additional file 5: Figure S4. Subgrouping prediction tasks outperform cohort-specific random background prediction tasks. For each of the learning tasks completed in METABRIC(LumA) to predict the presence of an actual mutation or subgrouping of mutations, five additional tasks were performed based on predicting a simulated set of point mutations. These sets were constructed by randomly selecting a group of samples from the entire cohort of the same size as the group of samples affected by the original “real” mutation in the cohort. In addition to these cohort-specific random subgroupings, we also constructed five gene-specific random subgroupings for each task by selecting samples as above using only those samples carrying any point mutation of the gene associated with the task’s subgrouping. (left) We compare the AUCs of cohort-specific random subgroupings to the AUCs of the original subgroupings. (right) We compare the AUC of each original subgrouping to the best AUC across the gene-specific random subgroupings of the same number of “mutated” samples. We include here these figures for the METABRIC-(LumA) and TCGA-BRCA(LumA) cohorts and for the remaining cohorts at our data portal under Figures/S4 - Cohort Null Background AUC Comparisons. The names of these figures have the format (expr-source)__(cohort)__cohort-comparison_Ridge.svg.

补充文件5：图S4。亚组预测任务优于队列特异性随机背景预测任务。针对在METABRIC(LumA)队列中开展的、用于预测真实突变或突变亚组存在情况的每项学习任务，本研究额外设置了5项基于模拟点突变集的预测任务。此类模拟突变集的构建方式为：从全队列中随机选取与队列中原始“真实”突变所影响的样本组规模一致的样本子集。除上述队列特异性随机亚组外，本研究还针对每项任务构建了5项基因特异性随机亚组：仅选取携带与该任务亚组相关基因的任意点突变的样本，并按照前述规则选取规模匹配的样本子集。（左图）我们将队列特异性随机亚组的受试者工作特征曲线下面积（Area Under Curve, AUC）与原始亚组的AUC值进行了对比。（右图）我们将每个原始亚组的AUC值与相同“突变”样本规模下基因特异性随机亚组中的最优AUC值进行了对比。本研究在此处提供了METABRIC-(LumA)、TCGA-BRCA(LumA)队列以及数据门户中其余队列的对应图表，相关图表位于路径Figures/S4 - Cohort Null Background AUC Comparisons下。此类图表的命名格式为：(expr-source)__(cohort)__cohort-comparison_Ridge.svg。