Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).
Identifying tightly regulated and variably expressed networks by Differential Rank Conservation (DIRAC).
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DOI：
10.1371/journal.pcbi.1000792
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发表时间：
2010-05-27
影响因子：
4.3
通讯作者：
Geman D
中科院分区：
文献类型：
--
作者：
Eddy JA;Hood L;Price ND;Geman D
在生物学中分离信号和噪声的一个有效方法是将来自单个基因或蛋白质的分子数据转换为对比较生物网络行为的分析。以前的网络分析的局限性之一是它们没有考虑网络中基因相互作用的组合性质。我们在这里报告了一种新的技术，差分等级守恒(Dirac)，它允许人们评估这些组合相互作用，在比较意义上量化不同的生物路径或网络，并确定它们在经历相同疾病过程的不同个体中如何变化。这种方法是基于参与基因的相对表达值--即网络配置文件中表达的顺序。狄拉克提供了网络排名在所选表型的网络之间或所选网络的表型之间的差异的定量测量。我们检查了包括癌症亚型和神经紊乱在内的疾病表型，并确定了严格调控的网络，其定义是高度保守的转录顺序。有趣的是，我们观察到在更多的恶性表型和疾病的后期有放松网络调控的强烈趋势。在样本水平上，狄拉克可以检测到任何选定网络的表型之间的排名变化。可变表达网络代表了疾病状态之间在统计上的强健差异，并作为准确的分子分类的签名，验证了狄拉克捕获的关于表达模式的信息。重要的是，狄拉克不仅可以应用于转录数据，而且可以应用于任何顺序数据类型。医学的系统方法源于这样一个想法，即由于多个分子之间相互作用的净效应，患病细胞来自一个或多个扰动的生物网络；通过测量生物分子(例如，信使核糖核酸、蛋白质、代谢物)丰度的差异，我们可以识别网络状态的报告者，并揭示疾病的分子特征。然而，以前发表的网络分析的一个主要局限性是关注少数个体差异表达的基因，因此未能考虑组合相互作用。我们报告了一种新的技术，差分秩守恒，用于识别和测量网络级扰动。我们的等级保守指数完全基于参与基因的相对表达水平，并允许我们检测给定表型的网络之间和给定网络的表型之间的网络排序差异。在研究癌症亚型和神经紊乱时，我们确定了严格和松散调控的网络，如转录顺序保守水平所定义的，并观察到在更恶性的表型和疾病的后期有放松网络调控的强烈趋势。我们还证明，可变表达的网络代表了疾病状态之间的强健差异。
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. The systems approach to medicine derives from the idea that diseased cells arise from one or more perturbed biological networks due to the net effect of interactions among multiple molecular agents; by measuring differences in the abundance of biomolecules (e.g., mRNA, proteins, metabolites) we can identify reporters of network states and uncover molecular signatures of disease. However, a major limitation of previously published network analyses is the focus on small numbers of individual, differentially-expressed genes, hence the failure to take into account combinatorial interactions. We report a new technique, Differential Rank Conservation, for identifying and measuring network-level perturbations. Our rank conservation index is based entirely on the relative levels of expression for participating genes and allows us to detect differences in network orderings between networks for a given phenotype and between phenotypes for a given network. In examining cancer subtypes and neurological disorders, we identified networks that are tightly and loosely regulated, as defined by the level of conservation of transcript ordering, and observed a strong trend to looser network regulation in more malignant phenotypes and later stages of disease. We also demonstrate that variably expressed networks represent robust differences between disease states.
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影响因子：
30.8
作者：
Armstrong, SA;Staunton, JE;Korsmeyer, SJ
通讯作者：
Korsmeyer, SJ
前列腺癌的基因表达谱揭示了多个分子途径在转移过程中的参与。
影响因子：
3.8
作者：
Chandran, Uma R.;Ma, Changqing;Dhir, Rajiv;Bisceglia, Michelle;Lyons-Weiler, Maureen;Liang, Wenjing;Michalopoulos, George;Becich, Michael;Monzon, Federico A.
通讯作者：
Monzon, Federico A.
影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES
影响因子：
64.8
作者：
Lowe, SW;Cepero, E;Evan, G
通讯作者：
Evan, G
DOI：
10.1126/science.1164382
发表时间：
2008-09-26
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Parsons DW;Jones S;Zhang X;Lin JC;Leary RJ;Angenendt P;Mankoo P;Carter H;Siu IM;Gallia GL;Olivi A;McLendon R;Rasheed BA;Keir S;Nikolskaya T;Nikolsky Y;Busam DA;Tekleab H;Diaz LA Jr;Hartigan J;Smith DR;Strausberg RL;Marie SK;Shinjo SM;Yan H;Riggins GJ;Bigner DD;Karchin R;Papadopoulos N;Parmigiani G;Vogelstein B;Velculescu VE;Kinzler KW
通讯作者：
Kinzler KW