Comparative assessment of differential network analysis methods
Comparative assessment of differential network analysis methods
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DOI：
10.1093/bib/bbw061
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发表时间：
2017-09-01
影响因子：
9.5
通讯作者：
Leser, Ulf
中科院分区：
文献类型：
--
作者：
Lichtblau, Yvonne;Zimmermann, Karin;Leser, Ulf
差分网络分析（DiNA）是近年来出现的一类基于网络的生物信息学算法，它关注细胞两种状态（如健康和疾病）之间的网络拓扑结构差异，以识别区分生物过程的关键参与者。与传统的差异分析相反，DiNA识别分子之间相互作用的变化，而不是单个分子的变化。这种能力在效应物改变（例如突变）但其表达不变的情况下尤其重要。已经提出了一些不同的DiNA方法，但仍然缺乏对它们在不同环境中的性能的比较评估。在本文中，我们评估了10种不同的DiNA算法从转录组数据中恢复遗传关键参与者的能力。我们构建了高质量的调控网络，并用来自四种不同类型癌症的共表达数据丰富它们。接下来，我们使用黄金标准列表（GSL）对这些数据集应用DiNA算法的结果进行评估。我们发现，当地的DiNA算法一般是上级的全局算法，所有DiNA算法优于传统的差分表达式分析。我们还评估了DiNA方法在底层蜂窝网络中利用额外知识的能力。为此，我们用已知的调节性miRNA丰富了癌症类型特异性网络，并比较了有和没有miRNA的网络中的算法性能。我们发现，包括miRNAs一致，大大提高了几乎所有测试算法的性能。我们的研究结果强调了全面的细胞模型的组学数据分析的优势。
Differential network analysis (DiNA) denotes a recent class of network-based Bioinformatics algorithms which focus on the differences in network topologies between two states of a cell, such as healthy and disease, to identify key players in the discriminating biological processes. In contrast to conventional differential analysis, DiNA identifies changes in the interplay between molecules, rather than changes in single molecules. This ability is especially important in cases where effectors are changed, e.g. mutated, but their expression is not. A number of different DiNA approaches have been proposed, yet a comparative assessment of their performance in different settings is still lacking. In this paper, we evaluate 10 different DiNA algorithms regarding their ability to recover genetic key players from transcriptome data. We construct highquality regulatory networks and enrich them with co-expression data from four different types of cancer. Next, we assess the results of applying DiNA algorithms on these data sets using a gold standard list (GSL). We find that local DiNA algorithms are generally superior to global algorithms, and that all DiNA algorithms outperformconventional differential expression analysis. We also assess the ability of DiNA methods to exploit additional knowledge in the underlying cellular networks. To this end, we enrich the cancer-type specific networks with known regulatory miRNAs and compare the algorithms performance in networks with and without miRNA. We find that including miRNAs consistently and considerably improves the performance of almost all tested algorithms. Our results underline the advantages of comprehensive cell models for the analysis of-omics data.