State-of-the art selection methods fail to identify weak but cumulative effects of features found in many high-dimensional omics datasets. Nevertheless, these features play an important role in certain diseases. We present Netboost, a three-step dimension reduction technique. First, a boosting-based filter is combined with the topological overlap measure to identify the essential edges of the network. Second, sparse hierarchical clustering is applied on the selected edges to identify modules and finally module information is aggregated by the first principal components. We demonstrate the application of the newly developed Netboost in combination with CoxBoost for survival prediction of DNA methylation and gene expression data from 180 acute myeloid leukemia (AML) patients and show, based on cross-validated prediction error curve estimates, its prediction superiority over variable selection on the full dataset as well as over an alternative clustering approach. The identified signature related to chromatin modifying enzymes was replicated in an independent dataset, the phase II AMLSG 12-09 study. In a second application we combine Netboost with Random Forest classification and improve the disease classification error in RNA-sequencing data of Huntington’s disease mice. Netboost is a freely available Bioconductor R package for dimension reduction and hypothesis generation in high-dimensional omics applications.

中文翻译：

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Netboost：Boosting 支持的网络分析改善了急性髓系白血病和亨廷顿病的高维组学预测


最先进的选择方法无法识别许多高维组学数据集中发现的特征的微弱但累积的影响。然而，这些特征在某些疾病中发挥着重要作用。我们提出了 Netboost，一种三步降维技术。首先，基于增强的滤波器与拓扑重叠测量相结合，以识别网络的基本边缘。其次，在选定的边上应用稀疏层次聚类来识别模块，最后通过第一主成分聚合模块信息。我们展示了新开发的 Netboost 与 CoxBoost 结合用于 180 名急性髓系白血病 (AML) 患者的 DNA 甲基化和基因表达数据的生存预测，并根据交叉验证的预测误差曲线估计表明，其预测优于变量对完整数据集以及替代聚类方法的选择。已识别的与染色质修饰酶相关的特征在独立数据集中重复，即 II 期 AMLSG 12-09 研究。在第二个应用中，我们将 Netboost 与随机森林分类相结合，改善了亨廷顿病小鼠 RNA 测序数据中的疾病分类错误。 Netboost 是一个免费提供的 Bioconductor R 软件包，用于高维组学应用中的降维和假设生成。