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Iterative sub-network component analysis enables reconstruction of large scale genetic networks.

Jayavelu ND, Aasgaard LS, Bar N - BMC Bioinformatics (2015)

Bottom Line: However, the restrictions these conditions pose are not necessarily true from biological perspective and they force network size reduction, pruning potentially important components.To address this, we developed a novel, Iterative Sub-Network Component Analysis (ISNCA) for reconstructing networks at any size.We tested the ISNCA on real, large datasets using various NCA algorithms.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Sem Salandsvei 4, Trondheim, Norway. nareshd@ntnu.no.

ABSTRACT

Background: Network component analysis (NCA) became a popular tool to understand complex regulatory networks. The method uses high-throughput gene expression data and a priori topology to reconstruct transcription factor activity profiles. Current NCA algorithms are constrained by several conditions posed on the network topology, to guarantee unique reconstruction (termed compliancy). However, the restrictions these conditions pose are not necessarily true from biological perspective and they force network size reduction, pruning potentially important components.

Results: To address this, we developed a novel, Iterative Sub-Network Component Analysis (ISNCA) for reconstructing networks at any size. By dividing the initial network into smaller, compliant subnetworks, the algorithm first predicts the reconstruction of each subnetwork using standard NCA algorithms. It then subtracts from the reconstruction the contribution of the shared components from the other subnetwork. We tested the ISNCA on real, large datasets using various NCA algorithms. The size of the networks we tested and the accuracy of the reconstruction increased significantly. Importantly, FOXA1, ATF2, ATF3 and many other known key regulators in breast cancer could not be incorporated by any NCA algorithm because of the necessary conditions. However, their temporal activities could be reconstructed by our algorithm, and therefore their involvement in breast cancer could be analyzed.

Conclusions: Our framework enables reconstruction of large gene expression data networks, without reducing their size or pruning potentially important components, and at the same time rendering the results more biological plausible. Our ISNCA method is not only suitable for prediction of key regulators in cancer studies, but it can be applied to any high-throughput gene expression data.

No MeSH data available.


Related in: MedlinePlus

Graphical representation of ISNCA approach. ISNCA approach involves predict and update phases. Subscripts ui and ci represent unique and common components of subnetwork i, respectively. See ‘Methods’ for the formulations of A, E, and T
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Fig1: Graphical representation of ISNCA approach. ISNCA approach involves predict and update phases. Subscripts ui and ci represent unique and common components of subnetwork i, respectively. See ‘Methods’ for the formulations of A, E, and T

Mentions: We propose a novel algorithm, the iterative sub-network component analysis (ISNCA), that iterates between NCA compliant, overlapping sub-networks (Fig. 1). These sub-networks share common TGs in order to solve larger, and most importantly, NCA incompliant networks. In order to apply the ISNCA, we first divide the network into two compliant sub-networks. The expression and connectivity matrices for each sub-network can be represented by (3)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$ E_{1}=\left[\begin{array}{ll} E_{u1} \\ E_{c} \end{array}\right], E_{2}=\left[\begin{array}{ll} E_{u2} \\ E_{c} \end{array}\right] $$ \end{document}E1=Eu1Ec,E2=Eu2EcFig. 1


Iterative sub-network component analysis enables reconstruction of large scale genetic networks.

Jayavelu ND, Aasgaard LS, Bar N - BMC Bioinformatics (2015)

Graphical representation of ISNCA approach. ISNCA approach involves predict and update phases. Subscripts ui and ci represent unique and common components of subnetwork i, respectively. See ‘Methods’ for the formulations of A, E, and T
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4634733&req=5

Fig1: Graphical representation of ISNCA approach. ISNCA approach involves predict and update phases. Subscripts ui and ci represent unique and common components of subnetwork i, respectively. See ‘Methods’ for the formulations of A, E, and T
Mentions: We propose a novel algorithm, the iterative sub-network component analysis (ISNCA), that iterates between NCA compliant, overlapping sub-networks (Fig. 1). These sub-networks share common TGs in order to solve larger, and most importantly, NCA incompliant networks. In order to apply the ISNCA, we first divide the network into two compliant sub-networks. The expression and connectivity matrices for each sub-network can be represented by (3)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document} $$ E_{1}=\left[\begin{array}{ll} E_{u1} \\ E_{c} \end{array}\right], E_{2}=\left[\begin{array}{ll} E_{u2} \\ E_{c} \end{array}\right] $$ \end{document}E1=Eu1Ec,E2=Eu2EcFig. 1

Bottom Line: However, the restrictions these conditions pose are not necessarily true from biological perspective and they force network size reduction, pruning potentially important components.To address this, we developed a novel, Iterative Sub-Network Component Analysis (ISNCA) for reconstructing networks at any size.We tested the ISNCA on real, large datasets using various NCA algorithms.

View Article: PubMed Central - PubMed

Affiliation: Department of Chemical Engineering, Norwegian University of Science and Technology (NTNU), Sem Salandsvei 4, Trondheim, Norway. nareshd@ntnu.no.

ABSTRACT

Background: Network component analysis (NCA) became a popular tool to understand complex regulatory networks. The method uses high-throughput gene expression data and a priori topology to reconstruct transcription factor activity profiles. Current NCA algorithms are constrained by several conditions posed on the network topology, to guarantee unique reconstruction (termed compliancy). However, the restrictions these conditions pose are not necessarily true from biological perspective and they force network size reduction, pruning potentially important components.

Results: To address this, we developed a novel, Iterative Sub-Network Component Analysis (ISNCA) for reconstructing networks at any size. By dividing the initial network into smaller, compliant subnetworks, the algorithm first predicts the reconstruction of each subnetwork using standard NCA algorithms. It then subtracts from the reconstruction the contribution of the shared components from the other subnetwork. We tested the ISNCA on real, large datasets using various NCA algorithms. The size of the networks we tested and the accuracy of the reconstruction increased significantly. Importantly, FOXA1, ATF2, ATF3 and many other known key regulators in breast cancer could not be incorporated by any NCA algorithm because of the necessary conditions. However, their temporal activities could be reconstructed by our algorithm, and therefore their involvement in breast cancer could be analyzed.

Conclusions: Our framework enables reconstruction of large gene expression data networks, without reducing their size or pruning potentially important components, and at the same time rendering the results more biological plausible. Our ISNCA method is not only suitable for prediction of key regulators in cancer studies, but it can be applied to any high-throughput gene expression data.

No MeSH data available.


Related in: MedlinePlus