Nonconvex Penalty Based Low-Rank Representation and Sparse Regression for eQTL Mapping
Authors:
- 27 September 2016
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Vol. 14 (5) , 1154-1164
Abstract
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.Keywords
Funding Information
- National Natural Science Foundation of China (61732012, 61520106006, 31571364, 61532008, U1611265, 61672382, 61402334, 61472280, 61472173, 61572447)
- China Postdoctoral Science Foundation (2014M561513, 2015M580352, 2017M611619, 2016M601646)
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