High-dimensional genomic data bias correction and data integration using MANCIE.
High-dimensional genomic data bias correction and data integration using MANCIE.
复制标题
使用 MANCIE 进行高维基因组数据偏差校正和数据集成
DOI：
10.1038/ncomms11305
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发表时间：
2016-04-13
影响因子：
16.6
通讯作者：
Liu XS
中科院分区：
文献类型：
--
作者：
Zang C;Wang T;Deng K;Li B;Hu S;Qin Q;Xiao T;Zhang S;Meyer CA;He HH;Brown M;Liu JS;Xie Y;Liu XS
由于高通量实验中的噪声和偏差，高维基因组数据分析具有挑战性。我们提出了一种计算方法矩阵分析和归一化的一致性信息增强（MANCIE）的偏差校正和数据整合不同的基因组图谱上相同的样品。MANCIE使用贝叶斯支持的基于主成分分析的方法来调整数据，以便在不同配置文件中的样本距离之间实现更好的一致性。MANCIE可以提高ENCODE数据的组织特异性聚类、乳腺癌国际联盟分子分类学和癌症基因组图谱数据的预后预测、癌细胞系百科全书数据的拷贝数和表达一致性，在跨平台、高维数据整合中具有广泛的应用。 鉴于大数据维度，对来自高通量基因组技术的数据的分析具有挑战性。在这里，Liu及其同事描述了一种名为MANCIE（Matrix Analysis and Normalization by Concordant Information Enhancement）的方法，该方法可以进行基因组数据归一化和偏差校正，以检测生物相关信息。
High-dimensional genomic data analysis is challenging due to noises and biases in high-throughput experiments. We present a computational method matrix analysis and normalization by concordant information enhancement (MANCIE) for bias correction and data integration of distinct genomic profiles on the same samples. MANCIE uses a Bayesian-supported principal component analysis-based approach to adjust the data so as to achieve better consistency between sample-wise distances in the different profiles. MANCIE can improve tissue-specific clustering in ENCODE data, prognostic prediction in Molecular Taxonomy of Breast Cancer International Consortium and The Cancer Genome Atlas data, copy number and expression agreement in Cancer Cell Line Encyclopedia data, and has broad applications in cross-platform, high-dimensional data integration. Analyses of data from high-throughput genomic technologies are challenging given large data dimensionality. Here, Liu and colleagues describe a method called MANCIE (Matrix Analysis and Normalization by Concordant Information Enhancement) that can conduct genomic data normalization and bias correction to detect biologically relevant information.