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Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs
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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8756181/ https://www.ncbi.nlm.nih.gov/pubmed/34718410 http://dx.doi.org/10.1093/bioinformatics/btab751 |
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author | Leng, Jiacheng Wu, Ling-Yun |
author_facet | Leng, Jiacheng Wu, Ling-Yun |
author_sort | Leng, Jiacheng |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8756181 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-87561812022-01-13 Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs Leng, Jiacheng Wu, Ling-Yun Bioinformatics Original Papers 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. Oxford University Press 2021-10-30 /pmc/articles/PMC8756181/ /pubmed/34718410 http://dx.doi.org/10.1093/bioinformatics/btab751 Text en © The Author(s) 2021. Published by Oxford University Press. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Original Papers Leng, Jiacheng Wu, Ling-Yun Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs |
title | Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs |
title_full | Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs |
title_fullStr | Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs |
title_full_unstemmed | Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs |
title_short | Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs |
title_sort | importance-penalized joint graphical lasso (ipjgl): differential network inference via ggms |
topic | Original Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8756181/ https://www.ncbi.nlm.nih.gov/pubmed/34718410 http://dx.doi.org/10.1093/bioinformatics/btab751 |
work_keys_str_mv | AT lengjiacheng importancepenalizedjointgraphicallassoipjgldifferentialnetworkinferenceviaggms AT wulingyun importancepenalizedjointgraphicallassoipjgldifferentialnetworkinferenceviaggms |