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Leng J, Wu LY. Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs. Bioinformatics 2021; 38:770-777. [PMID: 34718410 PMCID: PMC8756181 DOI: 10.1093/bioinformatics/btab751] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 10/03/2021] [Accepted: 10/27/2021] [Indexed: 02/03/2023] Open
Abstract
MOTIVATION Differential network inference is a fundamental and challenging problem to reveal gene interactions and regulation relationships under different conditions. Many algorithms have been developed for this problem; however, they do not consider the differences between the importance of genes, which may not fit the real-world situation. Different genes have different mutation probabilities, and the vital genes associated with basic life activities have less fault tolerance to mutation. Equally treating all genes may bias the results of differential network inference. Thus, it is necessary to consider the importance of genes in the models of differential network inference. RESULTS Based on the Gaussian graphical model with adaptive gene importance regularization, we develop a novel Importance-Penalized Joint Graphical Lasso method (IPJGL) for differential network inference. The presented method is validated by the simulation experiments as well as the real datasets. Furthermore, to precisely evaluate the results of differential network inference, we propose a new metric named APC2 for the differential levels of gene pairs. We apply IPJGL to analyze the TCGA colorectal and breast cancer datasets and find some candidate cancer genes with significant survival analysis results, including SOST for colorectal cancer and RBBP8 for breast cancer. We also conduct further analysis based on the interactions in the Reactome database and confirm the utility of our method. AVAILABILITY AND IMPLEMENTATION R source code of Importance-Penalized Joint Graphical Lasso is freely available at https://github.com/Wu-Lab/IPJGL. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Jiacheng Leng
- IAM, MADIS, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China,School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
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Cecchini G, Cestnik R, Pikovsky A. Impact of local network characteristics on network reconstruction. Phys Rev E 2021; 103:022305. [PMID: 33736016 DOI: 10.1103/physreve.103.022305] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 01/12/2021] [Indexed: 11/07/2022]
Abstract
When a network is inferred from data, two types of errors can occur: false positive and false negative conclusions about the presence of links. We focus on the influence of local network characteristics on the probability α of false positive conclusions, and on the probability β of false negative conclusions, in the case of networks of coupled oscillators. We demonstrate that false conclusion probabilities are influenced by local connectivity measures such as the shortest path length and the detour degree, which can also be estimated from the inferred network when the true underlying network is not known a priori. These measures can then be used for quantification of the confidence level of link conclusions, and for improving the network reconstruction via advanced concepts of link weights thresholding.
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Affiliation(s)
- Gloria Cecchini
- CSDC, Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Florence, Italy.,Institute of Physics and Astronomy, University of Potsdam, Campus Golm, Karl-Liebknecht-Straße 24/25, 14476 Potsdam-Golm, Germany
| | - Rok Cestnik
- Institute of Physics and Astronomy, University of Potsdam, Campus Golm, Karl-Liebknecht-Straße 24/25, 14476 Potsdam-Golm, Germany.,Faculty of Behavioural and Movement Sciences, Vrije Universiteit Amsterdam, Van der Boechorststraat 7, 1081 BT Amsterdam, Netherlands
| | - Arkady Pikovsky
- Institute of Physics and Astronomy, University of Potsdam, Campus Golm, Karl-Liebknecht-Straße 24/25, 14476 Potsdam-Golm, Germany.,Department of Control Theory, Lobachevsky University of Nizhny Novgorod, Gagarin Av. 23, 603950, Nizhny Novgorod, Russia
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Leguia MG, Martínez CGB, Malvestio I, Campo AT, Rocamora R, Levnajić Z, Andrzejak RG. Inferring directed networks using a rank-based connectivity measure. Phys Rev E 2019; 99:012319. [PMID: 30780311 DOI: 10.1103/physreve.99.012319] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2018] [Indexed: 11/07/2022]
Abstract
Inferring the topology of a network using the knowledge of the signals of each of the interacting units is key to understanding real-world systems. One way to address this problem is using data-driven methods like cross-correlation or mutual information. However, these measures lack the ability to distinguish the direction of coupling. Here, we use a rank-based nonlinear interdependence measure originally developed for pairs of signals. This measure not only allows one to measure the strength but also the direction of the coupling. Our results for a system of coupled Lorenz dynamics show that we are able to consistently infer the underlying network for a subrange of the coupling strength and link density. Furthermore, we report that the addition of dynamical noise can benefit the reconstruction. Finally, we show an application to multichannel electroencephalographic recordings from an epilepsy patient.
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Affiliation(s)
- Marc G Leguia
- Faculty of Information Studies, 8000 Novo Mesto, Slovenia.,Department of Communication and Information Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain
| | - Cristina G B Martínez
- Department of Communication and Information Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain
| | - Irene Malvestio
- Department of Communication and Information Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain.,Department of Physics and Astronomy, University of Florence, 50119 Sesto Fiorentino, Italy.,Institute for Complex Systems, CNR, 50119 Sesto Fiorentino, Italy
| | - Adrià Tauste Campo
- Center for Brain and Cognition, Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain.,Epilepsy Unit, Department of Neurology, IMIM Hospital del Mar, Universitat Pompeu Fabra, 08003 Barcelona, Spain.,Barcelonaβeta Brain Research Center, Pasqual Maragall Foundation, 08005 Barcelona, Spain
| | - Rodrigo Rocamora
- Epilepsy Unit, Department of Neurology, IMIM Hospital del Mar, Universitat Pompeu Fabra, 08003 Barcelona, Spain.,Faculty of Health and Life Sciences, Universitat Pompeu Fabra, 08003 Barcelona, Spain
| | - Zoran Levnajić
- Faculty of Information Studies, 8000 Novo Mesto, Slovenia.,Institute Jozef Stefan, 1000 Ljubljana, Slovenia
| | - Ralph G Andrzejak
- Department of Communication and Information Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain.,Institute for Bioengineering of Catalonia (IBEC), The Barcelona Institute of Science and Technology, Baldiri Reixac 10-12, 08028 Barcelona, Spain
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