Due to the advance in technology, the type of data is getting more complicated and large-scale. To analyze such complex data, more advanced technique is required. In case of omics data from two different groups, it is interesting to find significant biomarkers between two groups while controlling error rate such as false discovery rate (FDR). Over the last few decades, a lot of methods that control local false discovery rate have been developed, ranging from one-dimensional to k-dimensional FDR procedure. For comparison study, we select three of them, which have unique and significant properties: Efron et al. (2001), Ploner et al. (2006), and Kim et al. (2018) in chronological order. The first approach is one-dimensional approach while the other two are two-dimensional ones. Furthermore, we consider two more variants of Ploner’s approach. We compare the performance of those methods on both simulated and real data.

中文翻译：

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本地错误发现率控制方法的综合比较分析

由于技术的进步，数据的类型变得越来越复杂和大规模。为了分析这样的复杂数据，需要更高级的技术。在来自两个不同组的组学数据的情况下，有趣的是在两组之间找到重要的生物标记，同时控制错误率，例如错误发现率（FDR）。在过去的几十年中，已经开发了许多控制局部错误发现率的方法，从一维到k维FDR程序。为了进行比较研究，我们选择其中三个，它们具有独特而重要的特性：Efron等。（2001），Ploner等。（2006），以及Kim等。（2018）按时间顺序排列。第一种方法是一维方法，而其他两种是二维方法。此外，我们考虑了Ploner方法的两个变体。我们比较了这些方法在模拟和真实数据上的性能。