Partition decoupling for multi-gene analysis of gene expression profiling data.
Partition decoupling for multi-gene analysis of gene expression profiling data.
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DOI：
10.1186/1471-2105-12-497
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发表时间：
2011-12-30
影响因子：
3
通讯作者：
Rockmore D
中科院分区：
文献类型：
--
作者：
Braun R;Leibon G;Pauls S;Rockmore D
多基因相互作用可能在复杂表型的发展中发挥重要作用，并且相互作用基因之间的关系在微阵列分析中提出了具有挑战性的统计问题，因为参与这些相互作用的基因可能不表现出边缘差异表达。因此，有必要开发工具，可以识别相互作用的基因，区分表型，而不需要表型之间的分类边界是凸的。我们描述了一种新的无监督统计学习技术，称为分区解耦方法（PDM），基因表达微阵列数据的扩展和应用。该方法可用于基于多基因表达模式对样品进行分类，并鉴定与表型相关的途径，而不依赖于单个基因的差异表达。PDM使用迭代的谱聚类和擦洗步骤，在每次迭代中逐步揭示数据几何结构中更精细的结构。由于谱聚类具有辨别不可线性分离的聚类的能力，因此它能够阐明样本之间的关系，这些关系可能会被基于距离和树的分类器遗漏。在将数据投影到聚类质心上并计算残差（“擦洗”）之后，可以重复谱聚类，揭示在第一层中不可辨别的聚类。这些迭代中的每一个都提供了与其他数据解耦的数据分区，这些迭代被向前推进，直到残差中的结构与噪声无法区分，从而防止过拟合。我们详细描述了PDM，并将其应用于三个公开的癌症基因表达数据集。通过应用PDM上的路径的路径的基础上，并确定这些途径，允许无监督聚类的样本匹配已知的样本特征，我们展示了如何PDM可用于找到集的机械相关的基因，可能在疾病中发挥作用。可以下载执行PDM的R包。我们表明，PDM是一个有用的工具，从复杂的疾病，表型是不是线性可分离的多基因效应可能发挥作用的基因表达数据的分析。我们的研究结果表明，PDM能够区分细胞类型和治疗，其准确性高于通过其他方法获得的，并且Pathway-PDM应用程序是用于识别疾病相关通路的有价值的技术。
Multi-gene interactions likely play an important role in the development of complex phenotypes, and relationships between interacting genes pose a challenging statistical problem in microarray analysis, since the genes involved in these interactions may not exhibit marginal differential expression. As a result, it is necessary to develop tools that can identify sets of interacting genes that discriminate phenotypes without requiring that the classification boundary between phenotypes be convex. We describe an extension and application of a new unsupervised statistical learning technique, known as the Partition Decoupling Method (PDM), to gene expression microarray data. This method may be used to classify samples based on multi-gene expression patterns and to identify pathways associated with phenotype, without relying upon the differential expression of individual genes. The PDM uses iterated spectral clustering and scrubbing steps, revealing at each iteration progressively finer structure in the geometry of the data. Because spectral clustering has the ability to discern clusters that are not linearly separable, it is able to articulate relationships between samples that would be missed by distance- and tree-based classifiers. After projecting the data onto the cluster centroids and computing the residuals ("scrubbing"), one can repeat the spectral clustering, revealing clusters that were not discernible in the first layer. These iterations, each of which provide a partition of the data that is decoupled from the others, are carried forward until the structure in the residuals is indistinguishable from noise, preventing over-fitting. We describe the PDM in detail and apply it to three publicly available cancer gene expression data sets. By applying the PDM on a pathway-by-pathway basis and identifying those pathways that permit unsupervised clustering of samples that match known sample characteristics, we show how the PDM may be used to find sets of mechanistically-related genes that may play a role in disease. An R package to carry out the PDM is available for download. We show that the PDM is a useful tool for the analysis of gene expression data from complex diseases, where phenotypes are not linearly separable and multi-gene effects are likely to play a role. Our results demonstrate that the PDM is able to distinguish cell types and treatments with higher accuracy than is obtained through other approaches, and that the Pathway-PDM application is a valuable technique for identifying disease-associated pathways.
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影响因子：
4.3
作者：
Hudson NJ;Reverter A;Dalrymple BP
通讯作者：
Dalrymple BP
DOI：
10.1073/pnas.0802806106
发表时间：
2008-12-30
影响因子：
11.1
作者：
Leibon, Gregory;Pauls, Scott;Savell, Robert
通讯作者：
Savell, Robert
影响因子：
14.9
作者：
Kanehisa M;Araki M;Goto S;Hattori M;Hirakawa M;Itoh M;Katayama T;Kawashima S;Okuda S;Tokimatsu T;Yamanishi Y
通讯作者：
Yamanishi Y
影响因子：
12.3
作者：
Gentleman RC;Carey VJ;Bates DM;Bolstad B;Dettling M;Dudoit S;Ellis B;Gautier L;Ge Y;Gentry J;Hornik K;Hothorn T;Huber W;Iacus S;Irizarry R;Leisch F;Li C;Maechler M;Rossini AJ;Sawitzki G;Smith C;Smyth G;Tierney L;Yang JY;Zhang J
通讯作者：
Zhang J
影响因子：
56.9
作者：
Golub, TR;Slonim, DK;Lander, ES
通讯作者：
Lander, ES