A comparative study of rank aggregation methods for partial and top ranked lists in genomic applications.
A comparative study of rank aggregation methods for partial and top ranked lists in genomic applications.
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DOI：
10.1093/bib/bbx101
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发表时间：
2019-01-18
影响因子：
9.5
通讯作者：
Xiao G
中科院分区：
文献类型：
--
作者：
Li X;Wang X;Xiao G
排名聚合（RA）是将多个排名列表合并为单个排名的过程，在整合来自解决同一生物学问题的单个基因组研究的信息方面发挥了重要作用。在以前的研究中，注意力集中在汇总完整列表上。然而，部分和/或排名靠前的名单是普遍的，因为基因组研究的巨大异质性和有限的资源进行后续调查。为了能够处理这样的列表，过去已经提出了一些特别的调整，但是RA方法如何在它们上执行（调整后）从未被充分评估。在这篇文章中，提出了一个系统的框架来定义不同的情况下，可能会发生的基础上的性质，个别排名的名单。进行了全面的模拟研究，以检查现有的RA方法，适合于基因组应用程序的各种设置下模拟模拟实际情况下的集合的性能特征。提供了一个非小细胞肺癌数据示例以供进一步比较。基于我们的数值结果，一般的指导方针，哪些方法执行最好/最差，在什么条件下，提供。此外，我们还讨论了影响不同方法性能的关键因素。
Rank aggregation (RA), the process of combining multiple ranked lists into a single ranking, has played an important role in integrating information from individual genomic studies that address the same biological question. In previous research, attention has been focused on aggregating full lists. However, partial and/or top ranked lists are prevalent because of the great heterogeneity of genomic studies and limited resources for follow-up investigation. To be able to handle such lists, some ad hoc adjustments have been suggested in the past, but how RA methods perform on them (after the adjustments) has never been fully evaluated. In this article, a systematic framework is proposed to define different situations that may occur based on the nature of individually ranked lists. A comprehensive simulation study is conducted to examine the performance characteristics of a collection of existing RA methods that are suitable for genomic applications under various settings simulated to mimic practical situations. A non-small cell lung cancer data example is provided for further comparison. Based on our numerical results, general guidelines about which methods perform the best/worst, and under what conditions, are provided. Also, we discuss key factors that substantially affect the performance of the different methods.