Group sparse canonical correlation analysis for genomic data integration.
Group sparse canonical correlation analysis for genomic data integration.
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基因组数据整合的组稀疏规范相关分析。
DOI：
10.1186/1471-2105-14-245
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发表时间：
2013-08-12
影响因子：
3
通讯作者：
Wang YP
中科院分区：
文献类型：
--
作者：
Lin D;Zhang J;Li J;Calhoun VD;Deng HW;Wang YP
来自不同来源和平台（例如基因表达、单核苷酸多态性（SNP）和拷贝数变异（CNV））的高通量基因组数据集的出现极大地增强了我们对这些基因组因素之间的相互作用及其对复杂疾病的影响的理解。探索这些不同类型的基因组数据集之间的关系具有挑战性。在本文中，我们重点研究一种多元统计方法，即典型相关分析（CCA）方法来解决这个问题。如果数据样本的数量明显少于生物标志物的数量，传统的 CCA 方法就无法有效发挥作用，这是基因组数据（例如 SNP）的典型情况。稀疏CCA（sCCA）方法被引入来克服这个困难，主要使用l-1范数（CCA-l1）或l-1和l-2范数的组合（CCA-弹性网络）的惩罚。然而，他们在分析中忽略了基因组数据中的结构或群体效应，这些效应通常存在且很重要（例如，跨越基因的 SNP 作为一个群体相互作用并协同工作）。我们提出了一种新的组稀疏 CCA 方法（CCA-稀疏组）以及有效的数值算法来研究两种不同类型的基因组数据（即 SNP 和基因表达）之间的相互关系。然后，我们将该模型扩展到更通用的公式，其中可以包括现有的 sCCA 模型。我们将该模型应用于两个数据集中的特征/变量选择，并将我们的组稀疏 CCA 方法与现有的 sCCA 方法在模拟和两个真实数据集（人类胶质瘤数据和 NCI60 数据）上进行比较。我们使用具有一对典型变量的样本的图形表示来演示所选特征的区分特征。进一步进行通路分析以对这些特征进行生物学解释。 CCA-稀疏群体方法将特征的群体效应纳入相关性分析，同时进行个体特征选择。它优于两种 sCCA 方法（CCA-l1 和 CCA-group），通过识别具有更多真实阳性的相关特征，同时将模拟数据的总不一致控制在较低水平，即使不存在群体效应或存在与真实相关特征分组的不相关特征。与我们提出的CCA组稀疏模型相比，CCA-l1倾向于选择不太真实的相关特征，而CCA组倾向于选择更多冗余特征。
The emergence of high-throughput genomic datasets from different sources and platforms (e.g., gene expression, single nucleotide polymorphisms (SNP), and copy number variation (CNV)) has greatly enhanced our understandings of the interplay of these genomic factors as well as their influences on the complex diseases. It is challenging to explore the relationship between these different types of genomic data sets. In this paper, we focus on a multivariate statistical method, canonical correlation analysis (CCA) method for this problem. Conventional CCA method does not work effectively if the number of data samples is significantly less than that of biomarkers, which is a typical case for genomic data (e.g., SNPs). Sparse CCA (sCCA) methods were introduced to overcome such difficulty, mostly using penalizations with l-1 norm (CCA-l1) or the combination of l-1and l-2 norm (CCA-elastic net). However, they overlook the structural or group effect within genomic data in the analysis, which often exist and are important (e.g., SNPs spanning a gene interact and work together as a group). We propose a new group sparse CCA method (CCA-sparse group) along with an effective numerical algorithm to study the mutual relationship between two different types of genomic data (i.e., SNP and gene expression). We then extend the model to a more general formulation that can include the existing sCCA models. We apply the model to feature/variable selection from two data sets and compare our group sparse CCA method with existing sCCA methods on both simulation and two real datasets (human gliomas data and NCI60 data). We use a graphical representation of the samples with a pair of canonical variates to demonstrate the discriminating characteristic of the selected features. Pathway analysis is further performed for biological interpretation of those features. The CCA-sparse group method incorporates group effects of features into the correlation analysis while performs individual feature selection simultaneously. It outperforms the two sCCA methods (CCA-l1 and CCA-group) by identifying the correlated features with more true positives while controlling total discordance at a lower level on the simulated data, even if the group effect does not exist or there are irrelevant features grouped with true correlated features. Compared with our proposed CCA-group sparse models, CCA-l1 tends to select less true correlated features while CCA-group inclines to select more redundant features.
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