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07 March 2019
A Novel Method of Gene Regulatory Network Structure Inference from Gene Knock-Out Expression Data
Min Li(
),
),
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Chen X, Li M, Zheng R, et al.
A Novel Method of Gene Regulatory Network Structure Inference from Gene Knock-Out Expression Data.
Tsinghua Science and Technology,
2019, 24(4): 446-455.
https://doi.org/10.26599/TST.2018.9010097
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Inferring Gene Regulatory Networks (GRNs) structure from gene expression data has been a challenging problem in systems biology. It is critical to identify complicated regulatory relationships among genes for understanding regulatory mechanisms in cells. Various methods based on information theory have been developed to infer GRNs. However, these methods introduce many redundant regulatory relationships in the network inference process due to external noise in the original data, topology sparseness in the network structure, and non-linear dependency among genes. Especially as the network size increases, the performance of these methods decreases dramatically. In this paper, a novel network structure inference method named Loc-PCA-CMI is proposed that first identifies local overlapped gene clusters, and then infers the local network structure for each cluster by a Path Consistency Algorithm based on Conditional Mutual Information (PCA-CMI). The final structure of the GRN is denoted as dependence among genes by an ensemble of the obtained local network structures. Loc-PCA-CMI was evaluated on DREAM3 knock-out datasets, and its performance was compared to other information theory-based network inference methods including ARACNE, MRNET, PCA-CMI, and PCA-PMI. Experimental results demonstrate our novel method Loc-PCA-CMI outperforms the other four methods in DREAM3 datasets especially in size 50 and 100 networks.
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A Novel Method of Gene Regulatory Network Structure Inference from Gene Knock-Out Expression Data
Min Li(
),
),
Abstract
Inferring Gene Regulatory Networks (GRNs) structure from gene expression data has been a challenging problem in systems biology. It is critical to identify complicated regulatory relationships among genes for understanding regulatory mechanisms in cells. Various methods based on information theory have been developed to infer GRNs. However, these methods introduce many redundant regulatory relationships in the network inference process due to external noise in the original data, topology sparseness in the network structure, and non-linear dependency among genes. Especially as the network size increases, the performance of these methods decreases dramatically. In this paper, a novel network structure inference method named Loc-PCA-CMI is proposed that first identifies local overlapped gene clusters, and then infers the local network structure for each cluster by a Path Consistency Algorithm based on Conditional Mutual Information (PCA-CMI). The final structure of the GRN is denoted as dependence among genes by an ensemble of the obtained local network structures. Loc-PCA-CMI was evaluated on DREAM3 knock-out datasets, and its performance was compared to other information theory-based network inference methods including ARACNE, MRNET, PCA-CMI, and PCA-PMI. Experimental results demonstrate our novel method Loc-PCA-CMI outperforms the other four methods in DREAM3 datasets especially in size 50 and 100 networks.
Keywords:
gene regulatory networks, network inference, path consistency algorithm
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Publication history
Received: 05 April 2018
Accepted: 01 May 2018
Published:
07 March 2019
Issue date: August 2019
Copyright
© The author(s) 2019
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (Nos. 61622213 and 61732009), the 111 Project (No. B18059), and the Hunan Provincial Science and Technology Program (No. 2018WK4001).
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