netDx: Interpretable patient classification using integrated patient similarity networks

Docker image containing installed netDx software in Ubuntu to reproduce examples from the published manuscript. The R implementation of netDx is hosted at: https://github.com/BaderLab/netDx ---<br> Publication abstract: Patient classification has widespread biomedical and clinical applications, including diagnosis, prognosis and treatment response prediction. A clinically useful prediction algorithm should be accurate, generalizable, be able to integrate diverse data types, and handle sparse data. A clinical predictor based on genomic data needs to be easily interpretable to drive hypothesis-driven research into new treatments. We describe netDx, a novel supervised patient classification framework based on patient similarity networks. netDx meets the above criteria and particularly excels at data integration and model interpretability. We compared classification performance of this method against other machine-learning algorithms, using a cancer survival benchmark with four cancer types, each requiring integration of up to six genomic and clinical data types. In these tests, netDx has significantly higher average performance than most other machine-learning approaches across most cancer types. In comparison to traditional machine learning-based patient classifiers, netDx results are more interpretable, visualizing the decision boundary in the context of patient similarity space. When patient similarity is defined by pathway-level gene expression, netDx identifies biological pathways important for outcome prediction, as demonstrated in diverse data sets of breast cancer and asthma. Thus, netDx can serve both as a patient classifier and as a tool for discovery of biological features characteristic of disease. We provide a freely available software implementation of netDx along with sample files and automation workflows in R.本Docker镜像基于Ubuntu系统构建，预装了netDx（netDx）软件，可复现已发表论文中的示例。netDx的R语言实现托管于：https://github.com/BaderLab/netDx --- 论文摘要：患者分类在生物医学与临床领域具有广泛应用，涵盖诊断、预后评估以及治疗响应预测等场景。一款具备临床实用价值的预测算法，应当具备准确性、泛化能力，能够整合多源异构数据，并可处理稀疏数据。基于基因组数据的临床预测工具，还需具备良好的可解释性，以支撑面向新型治疗方案的假说驱动型研究。 本文介绍了netDx——一种基于患者相似性网络的新型有监督患者分类框架。netDx满足上述所有要求，尤其在数据整合与模型可解释性方面表现突出。 我们以涵盖四种癌症类型的癌症生存基准数据集为测试集，对比了netDx与其他机器学习算法的分类性能，该基准数据集要求整合最多六种基因组与临床数据类型。测试结果显示，在多数癌症类型中，netDx的平均性能显著优于绝大多数其他机器学习方法。 与传统基于机器学习的患者分类器相比，netDx的结果更具可解释性，可在患者相似性空间中可视化决策边界。当患者相似性由通路水平的基因表达量定义时，netDx可识别出对结局预测至关重要的生物学通路，这一点在乳腺癌与哮喘的多组数据集研究中得到了验证。 综上，netDx既可作为患者分类工具，也可作为挖掘疾病特征性生物学标志物的研究工具。我们提供了基于R语言的netDx开源软件实现，附带示例文件与自动化工作流。