Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data.
Precision Lasso: accounting for correlations and linear dependencies in high-dimensional genomic data.
复制标题
DOI：
10.1093/bioinformatics/bty750
复制
发表时间：
2019-04-01
期刊：
影响因子：
--
通讯作者：
Xing EP
中科院分区：
文献类型：
--
作者：
Wang H;Lengerich BJ;Aragam B;Xing EP
发现遗传标记和表型之间联系的关联研究是生物信息学的核心。正则化回归的方法，例如套索的变体，在这项任务中很受欢迎。尽管这些方法在平均情况下具有良好的预测性能，但它们存在相关变量选择不稳定和线性因变量选择不一致的问题。不幸的是，正如我们的经验所证明的那样，基因组数据集中经常存在变量相关和线性相关的问题，导致经典的变量选择方法表现不佳。为了应对这些挑战，我们提出了精密套索。精密套索是一种套索变体，它通过由解释变量的协方差和逆协差阵控制的正则化来促进稀疏变量的选择。在具有高度相关和线性相依变量的模拟数据中，我们展示了它选择稳定和一致变量的能力。然后，我们展示了Precision Lasso从乳腺癌患者的转录图谱中选择有意义变量的有效性。我们的结果表明，在变量相关和线性相关的情况下，Precision Lasso优于常用的变量选择方法，如Lasso、弹性网络和最小最大凹形惩罚(MCP)回归。软件可在https://github.com/HaohanWang/thePrecisionLasso.上获得补充数据可在生物信息学在线上获得。
Association studies to discover links between genetic markers and phenotypes are central to bioinformatics. Methods of regularized regression, such as variants of the Lasso, are popular for this task. Despite the good predictive performance of these methods in the average case, they suffer from unstable selections of correlated variables and inconsistent selections of linearly dependent variables. Unfortunately, as we demonstrate empirically, such problematic situations of correlated and linearly dependent variables often exist in genomic datasets and lead to under-performance of classical methods of variable selection. To address these challenges, we propose the Precision Lasso. Precision Lasso is a Lasso variant that promotes sparse variable selection by regularization governed by the covariance and inverse covariance matrices of explanatory variables. We illustrate its capacity for stable and consistent variable selection in simulated data with highly correlated and linearly dependent variables. We then demonstrate the effectiveness of the Precision Lasso to select meaningful variables from transcriptomic profiles of breast cancer patients. Our results indicate that in settings with correlated and linearly dependent variables, the Precision Lasso outperforms popular methods of variable selection such as the Lasso, the Elastic Net and Minimax Concave Penalty (MCP) regression. Software is available at https://github.com/HaohanWang/thePrecisionLasso. Supplementary data are available at Bioinformatics online.
登录
查看更多内容
影响因子：
5.8
作者：
He, Qianchuan;Lin, Dan-Yu
通讯作者：
Lin, Dan-Yu
影响因子：
5.8
作者：
Posada, D;Crandall, KA
通讯作者：
Crandall, KA
影响因子：
3
作者：
Krämer N;Schäfer J;Boulesteix AL
通讯作者：
Boulesteix AL
影响因子：
48
作者：
Gonzalez-Perez A;Perez-Llamas C;Deu-Pons J;Tamborero D;Schroeder MP;Jene-Sanz A;Santos A;Lopez-Bigas N
通讯作者：
Lopez-Bigas N
DOI：
10.1137/s0895479897326432
发表时间：
1999-10-27
影响因子：
1.5
作者：
Golub, GH;Hansen, PC;O'Leary, DP
通讯作者：
O'Leary, DP