VARIABLE SELECTION FOR BART: AN APPLICATION TO GENE REGULATION
VARIABLE SELECTION FOR BART: AN APPLICATION TO GENE REGULATION
复制标题
DOI：
10.1214/14-aoas755
复制
发表时间：
2014-09-01
影响因子：
1.8
通讯作者：
Jensen, Shane T.
中科院分区：
文献类型：
--
作者：
Bleich, Justin;Kapelner, Adam;Jensen, Shane T.
我们认为发现基因调控网络的任务，这是定义为一组基因和相应的转录因子，调节其表达水平。这可以被看作是一个变量选择问题，可能具有高维性。变量选择在高维环境中尤其具有挑战性，在高维环境中，很难检测到细微的个体效应和预测因子之间的相互作用。贝叶斯加性回归树[BART，Ann. Appl. Stat. 4（2010）266-298]提供了一种新的非参数替代参数回归方法，例如套索或逐步回归，特别是当相关预测因子的数量相对于可用预测因子的总数是稀疏的并且基本关系是非线性的时。我们开发了一个原则性的置换为基础的推理方法，用于确定选定的预测的影响时，可能是真实的。更进一步，我们适应BART程序，将知情的变量重要性的先验信息。我们目前的模拟表明，我们的方法相比，有利的现有的参数和非参数程序在各种数据设置。为了证明我们的方法在生物学背景下的潜力，我们将其应用于推断酵母（酿酒酵母）中的基因调控网络的任务。我们发现，与其他变量选择方法相比，我们基于BART的程序最能够恢复信号最大的协变量子集。在这项工作中开发的方法在R包bartMachine中很容易获得。
We consider the task of discovering gene regulatory networks, which are defined as sets of genes and the corresponding transcription factors which regulate their expression levels. This can be viewed as a variable selection problem, potentially with high dimensionality. Variable selection is especially challenging in high-dimensional settings, where it is difficult to detect subtle individual effects and interactions between predictors. Bayesian Additive Regression Trees [BART, Ann. Appl. Stat. 4 (2010) 266-298] provides a novel nonparametric alternative to parametric regression approaches, such as the lasso or stepwise regression, especially when the number of relevant predictors is sparse relative to the total number of available predictors and the fundamental relationships are nonlinear. We develop a principled permutation-based inferential approach for determining when the effect of a selected predictor is likely to be real. Going further, we adapt the BART procedure to incorporate informed prior information about variable importance. We present simulations demonstrating that our method compares favorably to existing parametric and nonparametric procedures in a variety of data settings. To demonstrate the potential of our approach in a biological context, we apply it to the task of inferring the gene regulatory network in yeast (Saccharomyces cerevisiae). We find that our BART-based procedure is best able to recover the subset of covariates with the largest signal compared to other variable selection methods. The methods developed in this work are readily available in the R package bartMachine.