Institute for Genomic Biology, University of Illinois, Urbana, Illinois, United States of America.
PLoS Comput Biol. 2010 May 27;6(5):e1000792. doi: 10.1371/journal.pcbi.1000792.
A powerful way to separate signal from noise in biology is to convert the molecular data from individual genes or proteins into an analysis of comparative biological network behaviors. One of the limitations of previous network analyses is that they do not take into account the combinatorial nature of gene interactions within the network. We report here a new technique, Differential Rank Conservation (DIRAC), which permits one to assess these combinatorial interactions to quantify various biological pathways or networks in a comparative sense, and to determine how they change in different individuals experiencing the same disease process. This approach is based on the relative expression values of participating genes-i.e., the ordering of expression within network profiles. DIRAC provides quantitative measures of how network rankings differ either among networks for a selected phenotype or among phenotypes for a selected network. We examined disease phenotypes including cancer subtypes and neurological disorders and identified networks that are tightly regulated, as defined by high conservation of transcript ordering. Interestingly, we observed a strong trend to looser network regulation in more malignant phenotypes and later stages of disease. At a sample level, DIRAC can detect a change in ranking between phenotypes for any selected network. Variably expressed networks represent statistically robust differences between disease states and serve as signatures for accurate molecular classification, validating the information about expression patterns captured by DIRAC. Importantly, DIRAC can be applied not only to transcriptomic data, but to any ordinal data type.
一种从生物学中分离信号与噪声的有力方法是将单个基因或蛋白质的分子数据转化为对比较生物网络行为的分析。先前网络分析的一个局限性是，它们没有考虑网络中基因相互作用的组合性质。我们在这里报告一种新技术，即差异秩守恒（DIRAC），它允许人们评估这些组合相互作用，以定量比较各种生物学途径或网络，并确定它们在经历相同疾病过程的不同个体中如何变化。这种方法基于参与基因的相对表达值，即网络谱内的表达排序。DIRAC 提供了定量衡量网络排名在选定表型的网络之间或选定网络的表型之间差异的方法。我们检查了疾病表型，包括癌症亚型和神经紊乱，并确定了受转录排序高度保守定义的紧密调节的网络。有趣的是，我们观察到在更恶性的表型和疾病的后期阶段，网络调节更加宽松的强烈趋势。在样本水平上，DIRAC 可以检测到任何选定网络的表型之间排名的变化。差异表达网络代表疾病状态之间具有统计学意义的稳健差异，并且作为准确分子分类的特征，验证了 DIRAC 捕获的表达模式信息。重要的是，DIRAC 不仅可以应用于转录组数据，还可以应用于任何有序数据类型。
