基于Lemon-Tree的集成多组学模块网络推理
Integrative multi-omics module network inference with Lemon-Tree
摘要：
模块网络推断是一种已建立的统计方法，用于从集成的多组学数据集重建共表达模块及其上游调控程序，所述集成的多组学数据集测量跨动态过程的不同个体、实验条件或时间点的各种细胞组分的活性水平。我们已经开发了Lemon-Tree，这是一个开源的、平台无关的、模块化的、可扩展的软件包，它实现了用于模块网络推理的最先进的集成方法。我们使用大规模肿瘤数据集对Lemon-Tree进行了基准测试，并表明Lemon-Tree算法与最先进的模块网络推理软件相比毫不逊色。我们还分析了来自The Cancer Genome Atlas的胶质母细胞瘤样本中测量的体细胞拷贝数改变和基因表达水平的大型数据集，发现Lemon-Tree正确识别了已知的胶质母细胞瘤癌基因和肿瘤抑制因子作为推断模块网络中的主调节因子。Lemon-Tree预测的新候选驱动基因使用肿瘤通路和存活分析进行验证。Lemon-Tree可以在GNU通用公共许可证2.0版下从http://lemon-tree.googlecode.com获得。
英文摘要：
Module network inference is an established statistical method to reconstruct co-expression modules and their upstream regulatory programs from integrated multi-omics datasets measuring the activity levels of various cellular components across different individuals, experimental conditions or time points of a dynamic process. We have developed Lemon-Tree, an open-source, platform-independent, modular, extensible software package implementing state-of-the-art ensemble methods for module network inference. We benchmarked Lemon-Tree using large-scale tumor datasets and showed that Lemon-Tree algorithms compare favorably with state-of-the-art module network inference software. We also analyzed a large dataset of somatic copy-number alterations and gene expression levels measured in glioblastoma samples from The Cancer Genome Atlas and found that Lemon-Tree correctly identifies known glioblastoma oncogenes and tumor suppressors as master regulators in the inferred module network. Novel candidate driver genes predicted by Lemon-Tree were validated using tumor pathway and survival analyses. Lemon-Tree is available from http://lemon-tree.googlecode.com under the GNU General Public License version 2.0.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
