Similarity network fusion for aggregating data types on a genomic scale
Similarity network fusion for aggregating data types on a genomic scale
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DOI：
10.1038/nmeth.2810
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发表时间：
2014-03-01
期刊：
影响因子：
48
通讯作者：
Goldenberg, Anna
中科院分区：
文献类型：
--
作者：
Wang, Bo;Mezlini, Aziz M.;Goldenberg, Anna
最近的技术使收集不同类型的全基因组数据具有成本效益。需要计算方法来将这些数据联合收割机结合起来，以创建给定疾病或生物过程的综合视图。相似性网络融合（SNF）通过构建样本网络（例如，例如，在一个实施例中，患者），然后有效地将这些数据融合到一个网络中，该网络代表了底层数据的全部频谱。例如，为了在给定患者队列的情况下创建疾病的综合视图，SNF计算并融合分别从每个数据类型获得的患者相似性网络，利用数据的互补性。我们使用SNF来结合联合收割机的mRNA表达，DNA甲基化和microRNA（miRNA）表达数据的五个癌症数据集。在识别癌症亚型时，SNF大大优于单一数据类型分析和已建立的综合方法，并可有效预测生存率。
Recent technologies have made it cost-effective to collect diverse types of genome-wide data. Computational methods are needed to combine these data to create a comprehensive view of a given disease or a biological process. Similarity network fusion (SNF) solves this problem by constructing networks of samples (e. g., patients) for each available data type and then efficiently fusing these into one network that represents the full spectrum of underlying data. For example, to create a comprehensive view of a disease given a cohort of patients, SNF computes and fuses patient similarity networks obtained from each of their data types separately, taking advantage of the complementarity in the data. We used SNF to combine mRNA expression, DNA methylation and microRNA (miRNA) expression data for five cancer data sets. SNF substantially outperforms single data type analysis and established integrative approaches when identifying cancer subtypes and is effective for predicting survival.