Size matters: how sample size affects the reproducibility and specificity of gene set analysis
Size matters: how sample size affects the reproducibility and specificity of gene set analysis
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DOI：
10.1186/s40246-019-0226-2
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发表时间：
2019-10-01
期刊：
影响因子：
4.5
通讯作者：
Kusalik, Anthony J.
中科院分区：
文献类型：
--
作者：
Maleki, Farhad;Ovens, Katie;Kusalik, Anthony J.
背景基因集分析是解释高通量基因表达研究数据的一种成熟的方法。在这类研究中，获得可重复的结果是一项基本要求。基因表达实验中影响重复性的一个因素是样本量的选择。然而，选择合适的样本量可能很困难，特别是因为选择可能取决于方法。此外，样本量的选择可能会对特异性产生意想不到的影响。结果在本文中，我们报道了一种系统、定量的方法来研究样本大小对13种基因集分析方法结果的重复性的影响。我们还研究了样本大小对这些方法的特异性的影响。该方法不依赖于合成数据，而是使用真实的表情数据集来提供准确可靠的评估。结论总体上，随着样本量的增加，基因集分析结果的重复性更强。然而，重复性的程度和增加的速度因方法不同而不同。此外，即使在没有差异表达的情况下，一些基因集分析方法也会报告大量的假阳性，而增加样本量并不能减少这些假阳性。本研究的结果可用于从现有的基因集合分析方法中选择一种方法。
Background Gene set analysis is a well-established approach for interpretation of data from high-throughput gene expression studies. Achieving reproducible results is an essential requirement in such studies. One factor of a gene expression experiment that can affect reproducibility is the choice of sample size. However, choosing an appropriate sample size can be difficult, especially because the choice may be method-dependent. Further, sample size choice can have unexpected effects on specificity. Results In this paper, we report on a systematic, quantitative approach to study the effect of sample size on the reproducibility of the results from 13 gene set analysis methods. We also investigate the impact of sample size on the specificity of these methods. Rather than relying on synthetic data, the proposed approach uses real expression datasets to offer an accurate and reliable evaluation. Conclusion Our findings show that, as a general pattern, the results of gene set analysis become more reproducible as sample size increases. However, the extent of reproducibility and the rate at which it increases vary from method to method. In addition, even in the absence of differential expression, some gene set analysis methods report a large number of false positives, and increasing sample size does not lead to reducing these false positives. The results of this research can be used when selecting a gene set analysis method from those available.