IEEE/ACM Trans Comput Biol Bioinform. 2017 Sep-Oct;14(5):1154-1164. doi: 10.1109/TCBB.2016.2609420. Epub 2016 Sep 27.
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.
本文针对 SNP-基因关联研究中混杂因素和表达数量性状基因座（eQTL）作图的问题，提出了一种利用非凸惩罚的低秩表示来控制混杂因素并利用稀疏回归进行 eQTL 作图的算法（NCLRS）。通过比较 NCLRS 和提出的算法在 18 个合成数据集上的结果，评估了所提出算法的效率。实验结果或生物数据集表明，与目前现有的方法相比，我们的方法是一种更有效的控制非遗传效应的工具。
