Seagull: lasso, group lasso and sparse-group lasso regularization for linear regression models via proximal gradient descent
Seagull: lasso, group lasso and sparse-group lasso regularization for linear regression models via proximal gradient descent
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DOI：
10.1186/s12859-020-03725-w
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发表时间：
2020-09-15
影响因子：
3
通讯作者：
Wittenburg, Doerte
中科院分区：
文献类型：
--
作者：
Klosa, Jan;Simon, Noah;Wittenburg, Doerte
背景生命科学中生物学问题的统计分析往往导致高维线性模型。为了求解相应的方程组，惩罚方法往往是可选择的方法。在多重共线性的情况下，它们尤其有用，如果解释变量的数量超过观察数量或由于某种生物学原因，就会出现多重共线性。然后，通过适当的兴趣函数对模型的拟合优度进行惩罚。突出的例子是套索、组合套索和稀疏组合套索。在这里，我们提供了一个快速和数值廉价的实现这些算子通过近端梯度下降。通过热启动实现了惩罚参数的网格搜索。通过回溯线搜索来确定连续迭代之间的步长。最后，海鸥--这里给出的R包--产生完整的正则化路径。结果利用公开获得的高维甲基化数据与已建立的R包SGL进行比较。这两个程序包的结果都使人们能够根据DNA甲基化状态准确预测生物年龄。但是，即使海沟SGL的结果非常相似(R-2和GT；0.99)，海沟计算的解只需要SGL所需时间的一小部分。此外，Seagull允许合并每个受处罚特征的权重。结论适用于海鸥线性回归模型的算子有套索、套索组合、稀疏套索和带惩罚因子的综合套索(IPF-LASSO)。因此，海鸥是套索变体的一个方便的信封。
Background Statistical analyses of biological problems in life sciences often lead to high-dimensional linear models. To solve the corresponding system of equations, penalization approaches are often the methods of choice. They are especially useful in case of multicollinearity, which appears if the number of explanatory variables exceeds the number of observations or for some biological reason. Then, the model goodness of fit is penalized by some suitable function of interest. Prominent examples are the lasso, group lasso and sparse-group lasso. Here, we offer a fast and numerically cheap implementation of these operators via proximal gradient descent. The grid search for the penalty parameter is realized by warm starts. The step size between consecutive iterations is determined with backtracking line search. Finally,seagull-the R package presented here- produces complete regularization paths. Results Publicly available high-dimensional methylation data are used to compareseagullto the established R packageSGL. The results of both packages enabled a precise prediction of biological age from DNA methylation status. But even though the results ofseagullandSGLwere very similar (R-2 > 0.99),seagullcomputed the solution in a fraction of the time needed bySGL. Additionally,seagullenables the incorporation of weights for each penalized feature. Conclusions The following operators for linear regression models are available inseagull: lasso, group lasso, sparse-group lasso and Integrative LASSO with Penalty Factors (IPF-lasso). Thus,seagullis a convenient envelope of lasso variants.