iOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery
iOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery
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DOI：
10.1038/s41540-019-0099-y
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发表时间：
2019-07-09
影响因子：
4
通讯作者：
Choi, Hyungwon
中科院分区：
文献类型：
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作者：
Koh, Hiromi W. L.;Fermin, Damian;Choi, Hyungwon
用于多组学数据整合的计算工具通常被设计用于解释大的表型变异的多组学特征的无监督检测。为了实现这一点，一些方法从联合统计误差模型中提取异质数据集中的潜在信号，而另一些方法则使用生物网络来传播差异表达信号并找到共识签名。然而，很少有方法直接考虑分子相互作用作为一个数据特征，不同组学数据集之间的重要连接器。连接不同分子水平的基因组规模相互作用组数据的日益可用性激发了一类新的方法从多组学数据中提取相互作用信号。在这里，我们开发了iOmicsPASS，这是一种在监督分析环境中搜索由相关组学数据类型内部和之间的分子相互作用组成的预测子网络的工具。基于用户提供的网络数据和相关组学数据集，iOmicsPASS计算每个分子相互作用的分数，并将修改的最近收缩质心算法应用于分数，以选择可以准确预测每个表型组的密集连接的子网络。iOmicsPASS检测一组稀疏的预测性分子相互作用，与替代方法相比，不会损失预测准确性，并且所选择的网络签名立即提供代表每个样品组的多组学特征的机械解释。广泛的模拟研究表明，互动级建模的明显好处。TCGA/CPTAC乳腺癌数据的iOmicsPASS分析还强调了作为阳性蛋白标记物的基底样亚型的新转录调控网络，这是通过分析个体组学数据看不到的结果。
Computational tools for multiomics data integration have usually been designed for unsupervised detection of multiomics features explaining large phenotypic variations. To achieve this, some approaches extract latent signals in heterogeneous data sets from a joint statistical error model, while others use biological networks to propagate differential expression signals and find consensus signatures. However, few approaches directly consider molecular interaction as a data feature, the essential linker between different omics data sets. The increasing availability of genome-scale interactome data connecting different molecular levels motivates a new class of methods to extract interactive signals from multiomics data. Here we developed iOmicsPASS, a tool to search for predictive subnetworks consisting of molecular interactions within and between related omics data types in a supervised analysis setting. Based on user-provided network data and relevant omics data sets, iOmicsPASS computes a score for each molecular interaction, and applies a modified nearest shrunken centroid algorithm to the scores to select densely connected subnetworks that can accurately predict each phenotypic group. iOmicsPASS detects a sparse set of predictive molecular interactions without loss of prediction accuracy compared to alternative methods, and the selected network signature immediately provides mechanistic interpretation of the multiomics profile representing each sample group. Extensive simulation studies demonstrate clear benefit of interaction-level modeling. iOmicsPASS analysis of TCGA/CPTAC breast cancer data also highlights new transcriptional regulatory network underlying the basal-like subtype as positive protein markers, a result not seen through analysis of individual omics data.