Controlling false discoveries in high-dimensional situations: boosting with stability selection.
Controlling false discoveries in high-dimensional situations: boosting with stability selection.
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DOI：
10.1186/s12859-015-0575-3
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发表时间：
2015-05-06
影响因子：
3
通讯作者：
Göker M
中科院分区：
文献类型：
--
作者：
Hofner B;Boccuto L;Göker M
现代生物技术常常产生比观测值包含更多变量的高维数据集（n≪p）。这些数据集对统计分析提出了新的挑战：变量选择成为这种设置中最重要的任务之一。在观察研究的现代数据集中也会出现类似的挑战，例如在生态学中，灵活的非线性模型适用于高维数据。我们评估了最近提出的可变选择的灵活框架，称为稳定性选择。通过使用重采样程序，稳定性选择为高维变量选择程序（如Lasso或boosting）增加了有限样本误差控制。我们考虑了增压和稳定性选择的组合，并给出了详细的模拟研究结果，为这种组合的有用性提供了见解。阐述了误差边界的解释，并给出了实际数据分析的见解。增强稳定性选择能够在高维环境中检测有影响的预测因子，同时在各种模拟场景中控制给定的误差范围。研究了算法对样本量、真正有影响的变量数或可调参数等参数的依赖关系。结果应用于研究自闭症谱系障碍患者的表型测量使用对数-线性相互作用模型，该模型通过促进拟合。稳定性选择鉴定出5条差异表达的氨基酸通路。稳定性选择是在免费的R包stab中实现的（http://CRAN.R-project.org/package=stabs）。事实证明，对于线性和加性模型，它在具有更多预测因子的高维环境下都能很好地工作。稳定性选择的原始版本控制了每个家族的错误率，这是相当保守的，但它的改进，互补对稳定性选择的情况要小得多。然而，应该注意适当地指定错误范围。本文的在线版本（doi:10.1186/s12859-015-0575-3）包含补充材料，授权用户可以使用。
Modern biotechnologies often result in high-dimensional data sets with many more variables than observations (n≪p). These data sets pose new challenges to statistical analysis: Variable selection becomes one of the most important tasks in this setting. Similar challenges arise if in modern data sets from observational studies, e.g., in ecology, where flexible, non-linear models are fitted to high-dimensional data. We assess the recently proposed flexible framework for variable selection called stability selection. By the use of resampling procedures, stability selection adds a finite sample error control to high-dimensional variable selection procedures such as Lasso or boosting. We consider the combination of boosting and stability selection and present results from a detailed simulation study that provide insights into the usefulness of this combination. The interpretation of the used error bounds is elaborated and insights for practical data analysis are given. Stability selection with boosting was able to detect influential predictors in high-dimensional settings while controlling the given error bound in various simulation scenarios. The dependence on various parameters such as the sample size, the number of truly influential variables or tuning parameters of the algorithm was investigated. The results were applied to investigate phenotype measurements in patients with autism spectrum disorders using a log-linear interaction model which was fitted by boosting. Stability selection identified five differentially expressed amino acid pathways. Stability selection is implemented in the freely available R package stabs (http://CRAN.R-project.org/package=stabs). It proved to work well in high-dimensional settings with more predictors than observations for both, linear and additive models. The original version of stability selection, which controls the per-family error rate, is quite conservative, though, this is much less the case for its improvement, complementary pairs stability selection. Nevertheless, care should be taken to appropriately specify the error bound. The online version of this article (doi:10.1186/s12859-015-0575-3) contains supplementary material, which is available to authorized users.
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影响因子：
5.8
作者：
He, Qianchuan;Lin, Dan-Yu
通讯作者：
Lin, Dan-Yu
挖掘表型以进行基因功能预测。
影响因子：
3
作者：
Groth P;Weiss B;Pohlenz HD;Leser U
通讯作者：
Leser U
影响因子：
6.1
作者：
Hothorn, Torsten;Mueller, Joerg;Brandl, Roland
通讯作者：
Brandl, Roland
影响因子：
4.5
作者：
Friedman, J;Hastie, T;Tibshirani, R
通讯作者：
Tibshirani, R
DOI：
10.1146/annurev-statistics-022513-115545
发表时间：
2014-01-01
期刊：
ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION, VOL 1
影响因子：
--
作者：
Buehlmann, Peter;Kalisch, Markus;Meier, Lukas
通讯作者：
Meier, Lukas