SCIA: A Novel Gene Set Analysis Applicable to Data With Different Characteristics
SCIA: A Novel Gene Set Analysis Applicable to Data With Different Characteristics
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SCIA：适用于不同特征数据的新型基因集分析
DOI：
10.3389/fgene.2019.00598
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发表时间：
2019-06-25
影响因子：
3.7
通讯作者：
Li, Yu
中科院分区：
文献类型：
--
作者：
Li, Yiqun;Wu, Ying;Li, Yu
基因集分析是功能富集和分子途径分析的常用方法。目前的大多数方法都是基于竞争性测试方法，假设每个基因都是独立的。然而，当竞争方法应用于具有高基因间相关性的数据集时，它们的错误发现率会被放大。自包含的测试方法可以解决这个问题，但对数据特征有其他限制。因此，需要一种统计上严格的测试方法，适用于具有各种复杂特征的不同数据集，以获得无偏和可比性的结果。为了缓解现有基因集分析方法应用范围有限所带来的偏差，我们提出了一种自包含竞争性整合分析方法（SCIA）。这是通过一种新的排列策略来实现的，该策略使用先验生物网络来选择性地以不同的概率排列基因标签。在仿真研究中，将SCIA与GSEA、CAMERA、ROAST和NES四种代表性分析方法进行了比较，在大多数条件下，不同参数设置下，SCIA在错误发现率和灵敏度方面都表现最佳。进一步，对两个真实的肺癌数据集进行KEGG通路分析，发现SCIA在这两个数据集上发现的结果都远远多于GSEA，并且大部分都可以得到文献的支持。总的来说，SCIA有望为研究人员提供更可靠和可比较的不同数据集的结果。
Gene set analysis is commonly used in functional enrichment and molecular pathway analyses. Most of the present methods are based on the competitive testing methods which assume each gene is independent of the others. However, the false discovery rates of competitive methods are amplified when they are applied to datasets with high inter-gene correlations. The self-contained testing methods could solve this problem, but there are other restrictions on data characteristics. Therefore, a statistically rigorous testing method applicable to different datasets with various complex characteristics is needed to obtain unbiased and comparable results. We propose a self-contained and competitive incorporated analysis (SCIA) to alleviate the bias caused by the limited application scope of existing gene set analysis methods. This is accomplished through a novel permutation strategy using a priori biological networks to selectively permute gene labels with different probabilities. In simulation studies, SCIA was compared with four representative analysis methods (GSEA, CAMERA, ROAST, and NES), and produced the best performance in both false discovery rate and sensitivity under most conditions with different parameter settings. Further, the KEGG pathway analysis on two real datasets of lung cancer showed that the results found by SCIA in both of the two datasets are much more than that of GSEA and most of them could be supported by literature. Overall, SCIA promisingly offers researchers more reliable and comparable results with different datasets.