Inferring the experimental design for accurate gene regulatory network inference

被引:5
作者
Secilmis, Deniz [1 ]
Hillerton, Thomas [1 ]
Nelander, Sven [2 ,3 ]
Sonnhammer, Erik L. L. [1 ]
机构
[1] Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, S-17121 Solna, Sweden
[2] Uppsala Univ, Dept Immunol Genet & Pathol, SE-75185 Uppsala, Sweden
[3] Uppsala Univ, Sci Life Lab, SE-75185 Uppsala, Sweden
关键词
D O I
10.1093/bioinformatics/btab367
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Motivation: Accurate inference of gene regulatory interactions is of importance for understanding the mechanisms of underlying biological processes. For gene expression data gathered from targeted perturbations, gene regulatory network (GRN) inference methods that use the perturbation design are the top performing methods. However, the connection between the perturbation design and gene expression can be obfuscated due to problems, such as experimental noise or off-target effects, limiting the methods' ability to reconstruct the true GRN. Results: In this study, we propose an algorithm, IDEMAX, to infer the effective perturbation design from gene expression data in order to eliminate the potential risk of fitting a disconnected perturbation design to gene expression. We applied IDEMAX to synthetic data from two different data generation tools, GeneNetWeaver and GeneSPIDER, and assessed its effect on the experiment design matrix as well as the accuracy of the GRN inference, followed by application to a real dataset. The results show that our approach consistently improves the accuracy of GRN inference compared to using the intended perturbation design when much of the signal is hidden by noise, which is often the case for real data.
引用
收藏
页码:3553 / 3559
页数:7
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