Integrating fMRI and SNP data for biomarker identification for schizophrenia with a sparse representation based variable selection method.
Integrating fMRI and SNP data for biomarker identification for schizophrenia with a sparse representation based variable selection method.
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DOI：
10.1186/1755-8794-6-s3-s2
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发表时间：
2013
影响因子：
2.7
通讯作者：
Wang YP
中科院分区：
文献类型：
--
作者：
Cao H;Duan J;Lin D;Calhoun V;Wang YP
近年来，单核苷酸多态性（SNP）芯片和功能磁共振成像（fMRI）技术在精神分裂症（SCZ）的研究中得到了广泛的应用。此外，一些研究已报告整合SNPs数据和fMRI数据进行综合分析。在这项研究中，一种新的稀疏表示为基础的变量选择（SRVS）方法已被提出和测试的模拟数据集上，以证明其多分辨率特性。然后应用SRVS方法对92例患者和116例对照者的单核苷酸多态性（SNP）数据集和功能性共振成像（fMRI）数据集进行综合分析。该疾病的生物标志物被鉴定并用多变量分类方法进行验证，然后进行留一法（LOO）交叉验证。然后，我们将结果与以前报道的基于稀疏表示的特征选择方法进行比较。结果表明，从我们提出的SRVS方法的生物标志物在区分SCZ患者从健康对照组比以前报道的稀疏表示方法的分类准确率显着更高。此外，使用来自两个数据集的生物标志物导致比使用单一类型的生物标志物更好的分类准确性，这表明了不同类型数据的综合分析的优势。所提出的SRVS算法在识别SCZ等复杂疾病的重要生物标志物方面是有效的。整合不同类型的数据（例如SNP和fMRI数据）可以识别互补的生物标志物，从而有助于疾病的诊断准确性。
In recent years, both single-nucleotide polymorphism (SNP) array and functional magnetic resonance imaging (fMRI) have been widely used for the study of schizophrenia (SCZ). In addition, a few studies have been reported integrating both SNPs data and fMRI data for comprehensive analysis. In this study, a novel sparse representation based variable selection (SRVS) method has been proposed and tested on a simulation data set to demonstrate its multi-resolution properties. Then the SRVS method was applied to an integrative analysis of two different SCZ data sets, a Single-nucleotide polymorphism (SNP) data set and a functional resonance imaging (fMRI) data set, including 92 cases and 116 controls. Biomarkers for the disease were identified and validated with a multivariate classification approach followed by a leave one out (LOO) cross-validation. Then we compared the results with that of a previously reported sparse representation based feature selection method. Results showed that biomarkers from our proposed SRVS method gave significantly higher classification accuracy in discriminating SCZ patients from healthy controls than that of the previous reported sparse representation method. Furthermore, using biomarkers from both data sets led to better classification accuracy than using single type of biomarkers, which suggests the advantage of integrative analysis of different types of data. The proposed SRVS algorithm is effective in identifying significant biomarkers for complicated disease as SCZ. Integrating different types of data (e.g. SNP and fMRI data) may identify complementary biomarkers benefitting the diagnosis accuracy of the disease.