A statistical framework for differential network analysis from microarray data.
A statistical framework for differential network analysis from microarray data.
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DOI：
10.1186/1471-2105-11-95
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发表时间：
2010-02-19
影响因子：
3
通讯作者：
Datta S
中科院分区：
文献类型：
--
作者：
Gill R;Datta S;Datta S
众所周知，基因并不是单独起作用的；相反，在生物过程中，成群的基因协同作用。因此，基因的表达水平是相互依赖的。检测这种相互作用的基因对的实验技术已经存在了相当长的一段时间。随着微阵列技术的出现，人们提出了新的计算技术来检测基因表达之间的相互作用或关联，从而形成了一个关联网络。虽然大多数微阵列分析寻找的是差异表达的基因，但识别整个关联网络结构在两种或更多生物环境（如正常细胞与患病细胞类型）之间如何变化，可能具有更大的意义。我们提供了一种在两种实验设置下对由微阵列数据构建的网络进行差异分析的配方。我们方法的核心是一个连接分数，它代表了两个基因之间的遗传关联或相互作用的强度。我们使用这个分数对以下每个查询提出正式的统计测试：(i)两个网络的整体模块化结构是否不同，（ii）特定一组“感兴趣的基因”的连通性是否在两个网络之间发生了变化，以及（iii）给定单个基因的连通性是否在两个网络之间发生了变化。这里提供了一些关于这个分数的例子。我们在两种类型的模拟数据上进行了我们的方法：高斯网络和基于微分方程的网络。我们表明，对于连接性分数和调优参数的适当选择，我们的方法在模拟数据上工作得很好。我们还分析了正常小鼠和肥胖小鼠的真实数据集，并确定了一组可能在肥胖中起关键作用的有趣基因。检查网络结构的变化可以为潜在的生化途径提供有价值的信息。具有适当连通性分数的差分网络分析是探索不同生物条件下网络结构变化的有用工具。我们的测试R包可以从补充网站http://www.somnathdatta.org/Supp/DNA下载。
It has been long well known that genes do not act alone; rather groups of genes act in consort during a biological process. Consequently, the expression levels of genes are dependent on each other. Experimental techniques to detect such interacting pairs of genes have been in place for quite some time. With the advent of microarray technology, newer computational techniques to detect such interaction or association between gene expressions are being proposed which lead to an association network. While most microarray analyses look for genes that are differentially expressed, it is of potentially greater significance to identify how entire association network structures change between two or more biological settings, say normal versus diseased cell types. We provide a recipe for conducting a differential analysis of networks constructed from microarray data under two experimental settings. At the core of our approach lies a connectivity score that represents the strength of genetic association or interaction between two genes. We use this score to propose formal statistical tests for each of following queries: (i) whether the overall modular structures of the two networks are different, (ii) whether the connectivity of a particular set of "interesting genes" has changed between the two networks, and (iii) whether the connectivity of a given single gene has changed between the two networks. A number of examples of this score is provided. We carried out our method on two types of simulated data: Gaussian networks and networks based on differential equations. We show that, for appropriate choices of the connectivity scores and tuning parameters, our method works well on simulated data. We also analyze a real data set involving normal versus heavy mice and identify an interesting set of genes that may play key roles in obesity. Examining changes in network structure can provide valuable information about the underlying biochemical pathways. Differential network analysis with appropriate connectivity scores is a useful tool in exploring changes in network structures under different biological conditions. An R package of our tests can be downloaded from the supplementary website http://www.somnathdatta.org/Supp/DNA.
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DOI：
10.1073/pnas.0303415101
发表时间：
2004-05-18
影响因子：
11.1
作者：
Weckwerth, W;Loureiro, ME;Fiehn, O
通讯作者：
Fiehn, O
影响因子：
3.5
作者：
Xiong, MA;Feghali-Bostwick, CA;Zhou, XD
通讯作者：
Zhou, XD
影响因子：
3
作者：
Van den Bulcke, T;Van Leemput, K;Naudts, B;van Remortel, P;Ma, HW;Verschoren, A;De Moor, B;Marchal, K
通讯作者：
Marchal, K
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
45.3
作者：
Juric, Dejan;Lacayo, Norman J.;Sikic, Branimir I.
通讯作者：
Sikic, Branimir I.