Large Causal Gene Regulatory Network Inference by Decomposition into Subnetworks

Leung-Yau LO, Man Leung WONG, Kin-Hong LEE, Kwong-Sak LEUNG

Research output: Journal PublicationsJournal Article (refereed)Researchpeer-review

Abstract

Inferring the gene regulatory network is an important first step toward understanding the work of the cell and consequently curing diseases related to malfunction of the cell. One thorny problem in gene regulatory network inference is that even with high throughput technology, the available time series expression data is still very limited compared to the network size. To alleviate this problem, we propose to decompose large network into small subnetworks without prior knowledge of the decomposition. Our algorithm first infers an initial GRN using CLINDE, decomposes it into possibly overlapping subnetworks, then infers each subnetwork by either CLINDE or DD-lasso and finally merges the subnetworks. We have tested this algorithm on synthetic data of networks with 500 and 1000 genes. The results show that our proposed algorithm does improve the GRN inference performance of using either CLINDE or DD-lasso alone on the large network, with statistical significance, and is robust to different variances and slight deviation from Gaussian distribution in error terms.
Original languageEnglish
Pages (from-to)175 - 183
JournalInternational Journal of Bioscience, Biochemistry and Bioinformatics
Volume5
Issue number3
DOIs
Publication statusPublished - May 2015

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Genes
Decomposition
Gaussian distribution
Curing
Time series
Throughput

Keywords

  • Decomposition
  • large gene network inference
  • time delay
  • time series expression data

Cite this

@article{b830e602961e4820842c2a317e8ca152,
title = "Large Causal Gene Regulatory Network Inference by Decomposition into Subnetworks",
abstract = "Inferring the gene regulatory network is an important first step toward understanding the work of the cell and consequently curing diseases related to malfunction of the cell. One thorny problem in gene regulatory network inference is that even with high throughput technology, the available time series expression data is still very limited compared to the network size. To alleviate this problem, we propose to decompose large network into small subnetworks without prior knowledge of the decomposition. Our algorithm first infers an initial GRN using CLINDE, decomposes it into possibly overlapping subnetworks, then infers each subnetwork by either CLINDE or DD-lasso and finally merges the subnetworks. We have tested this algorithm on synthetic data of networks with 500 and 1000 genes. The results show that our proposed algorithm does improve the GRN inference performance of using either CLINDE or DD-lasso alone on the large network, with statistical significance, and is robust to different variances and slight deviation from Gaussian distribution in error terms.",
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Large Causal Gene Regulatory Network Inference by Decomposition into Subnetworks. / LO, Leung-Yau; WONG, Man Leung; LEE, Kin-Hong; LEUNG, Kwong-Sak.

In: International Journal of Bioscience, Biochemistry and Bioinformatics, Vol. 5, No. 3, 05.2015, p. 175 - 183.

Research output: Journal PublicationsJournal Article (refereed)Researchpeer-review

TY - JOUR

T1 - Large Causal Gene Regulatory Network Inference by Decomposition into Subnetworks

AU - LO, Leung-Yau

AU - WONG, Man Leung

AU - LEE, Kin-Hong

AU - LEUNG, Kwong-Sak

PY - 2015/5

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N2 - Inferring the gene regulatory network is an important first step toward understanding the work of the cell and consequently curing diseases related to malfunction of the cell. One thorny problem in gene regulatory network inference is that even with high throughput technology, the available time series expression data is still very limited compared to the network size. To alleviate this problem, we propose to decompose large network into small subnetworks without prior knowledge of the decomposition. Our algorithm first infers an initial GRN using CLINDE, decomposes it into possibly overlapping subnetworks, then infers each subnetwork by either CLINDE or DD-lasso and finally merges the subnetworks. We have tested this algorithm on synthetic data of networks with 500 and 1000 genes. The results show that our proposed algorithm does improve the GRN inference performance of using either CLINDE or DD-lasso alone on the large network, with statistical significance, and is robust to different variances and slight deviation from Gaussian distribution in error terms.

AB - Inferring the gene regulatory network is an important first step toward understanding the work of the cell and consequently curing diseases related to malfunction of the cell. One thorny problem in gene regulatory network inference is that even with high throughput technology, the available time series expression data is still very limited compared to the network size. To alleviate this problem, we propose to decompose large network into small subnetworks without prior knowledge of the decomposition. Our algorithm first infers an initial GRN using CLINDE, decomposes it into possibly overlapping subnetworks, then infers each subnetwork by either CLINDE or DD-lasso and finally merges the subnetworks. We have tested this algorithm on synthetic data of networks with 500 and 1000 genes. The results show that our proposed algorithm does improve the GRN inference performance of using either CLINDE or DD-lasso alone on the large network, with statistical significance, and is robust to different variances and slight deviation from Gaussian distribution in error terms.

KW - Decomposition

KW - large gene network inference

KW - time delay

KW - time series expression data

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JO - International Journal of Bioscience, Biochemistry and Bioinformatics

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