We discuss the challenge of comparing three gene prioritization methods: network propagation, integer linear programming rank aggregation (RA), and statistical RA. These methods are based on different biological categories and estimate disease–gene association. Previously proposed comparison schemes are based on three measures of performance: receiver operating curve, area under the curve, and median rank ratio. Although they may capture important aspects of gene prioritization performance, they may fail to capture important differences in the rankings of individual genes. We suggest that comparison schemes could be improved by also considering recently proposed measures of similarity between gene rankings. We tested this suggestion on comparison schemes for prioritizations of genes associated with autism that were obtained using brain- and tissue-specific data. Our results show the effectiveness of our measures of similarity in clustering brain regions based on their relevance to autism.

中文翻译：

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比较基因优先级的等级相似度测量：自闭症案例研究

我们讨论了比较三种基因优先排序方法的挑战：网络传播、整数线性规划秩聚合 (RA) 和统计 RA。这些方法基于不同的生物学类别并估计疾病-基因关联。先前提出的比较方案基于三种性能度量：接收器操作曲线、曲线下面积和中值秩比。尽管它们可能捕捉到基因优先排序性能的重要方面，但它们可能无法捕捉到单个基因排名的重要差异。我们建议还可以通过考虑最近提出的基因排名之间相似性的度量来改进比较方案。我们在使用大脑和组织特定数据获得的与自闭症相关的基因的优先排序比较方案上测试了这一建议。我们的结果显示了我们的相似性度量在基于它们与自闭症的相关性对大脑区域进行聚类时的有效性。