用于基因集分析可重复性估计的基因集装袋
Gene set bagging for estimating replicability of gene set analyses
摘要：
背景资料：显著性分析在识别和排序基因、转录因子结合位点、DNA甲基化区域和其他与疾病相关的高通量特征方面起着重要作用。我们提出了一种新的方法，称为基因集装袋，使用预定义的基因集来衡量排名过程的稳定性。基因集装袋包括重新采样原始高通量数据，对重新采样的数据进行基因集分析，并确认生物类别复制。这个过程可以被认为是自举基因集分析，并可用于确定哪些是最可重复的基因集。结果：在这里，我们将这种方法应用于两个常见的基因组学应用：基因表达和DNA甲基化。即使使用最先进的统计排序程序，基因集富集分析中的重要类别在经历重新排序时也可能不稳定。结论：我们证明，基因列表不一定是稳定的，因此，额外的步骤，如基因集装袋可以提高基因集分析的生物推断。
英文摘要：
Background: Significance analysis plays a major role in identifying and ranking genes, transcription factor binding sites, DNA methylation regions, and other high-throughput features for association with disease. We propose a new approach, called gene set bagging, for measuring the stability of ranking procedures using predefined gene sets. Gene set bagging involves resampling the original high-throughput data, performing gene-set analysis on the resampled data, and confirming that biological categories replicate. This procedure can be thought of as bootstrapping gene-set analysis and can be used to determine which are the most reproducible gene sets. Results: Here we apply this approach to two common genomics applications: gene expression and DNA methylation. Even with state-of-the-art statistical ranking procedures, significant categories in a gene set enrichment analysis may be unstable when subjected to resampling. Conclusions: We demonstrate that gene lists are not necessarily stable, and therefore additional steps like gene set bagging can improve biological inference of gene set analysis.
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