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Nonconvex Penalty Based Low-Rank Representation and Sparse Regression for eQTL Mapping

机译:基于非凸罚分的低秩表示和稀疏回归用于eQTL映射

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摘要

This paper addresses the problem of accounting for confounding factors and expression quantitative trait loci (eQTL) mapping in the study of SNP-gene associations. The existing convex penalty based algorithm has limited capacity to keep main information of matrix in the process of reducing matrix rank. We present an algorithm, which use nonconvex penalty based low-rank representation to account for confounding factors and make use of sparse regression for eQTL mapping (NCLRS). The efficiency of the presented algorithm is evaluated by comparing the results of 18 synthetic datasets given by NCLRS and presented algorithm, respectively. The experimental results or biological dataset show that our approach is an effective tool to account for non-genetic effects than currently existing methods.
机译:本文研究了在SNP基因关联研究中考虑混杂因素和表达定量性状基因座(eQTL)定位的问题。现有的基于凸惩罚的算法在降低矩阵秩的过程中保留矩阵主信息的能力有限。我们提出了一种算法,该算法使用基于非凸罚分的低秩表示法来考虑混杂因素,并利用稀疏回归进行eQTL映射(NCLRS)。通过比较NCLRS和所提出算法分别提供的18个综合数据集的结果,评估了所提出算法的效率。实验结果或生物学数据集表明,与当前现有方法相比,我们的方法是解决非遗传效应的有效工具。

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  • 作者单位

    Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China;

    Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China;

    Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China;

    Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China;

    School of Information and Computer, Anhui Agricultural University, Hefei, Anhui, China;

    Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China;

    Institute of Machine Learning and Systems Biology, School of Electronics and Information Engineering, Tongji University, Shanghai, China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Gene expression; Sparse matrices; Minimization; Convex functions; Optimization; Algorithm design and analysis;

    机译:基因表达;稀疏矩阵;最小化;凸函数;优化;算法设计与分析;

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