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Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs
Leng, Jiacheng1,2; Wu, Ling-Yun1,2
Source PublicationBIOINFORMATICS
AbstractMotivation: 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.
Indexed BySCI
Funding ProjectNational Key Research and Development Program of China[2020YFA0712402] ; National Natural Science Foundation of China[11631014]
WOS Research AreaBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Computer Science ; Mathematical & Computational Biology ; Mathematics
WOS SubjectBiochemical Research Methods ; Biotechnology & Applied Microbiology ; Computer Science, Interdisciplinary Applications ; Mathematical & Computational Biology ; Statistics & Probability
WOS IDWOS:000743386000021
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Document Type期刊论文
Corresponding AuthorWu, Ling-Yun
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, NCMIS, MADIS,IAM, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Leng, Jiacheng,Wu, Ling-Yun. Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs[J]. BIOINFORMATICS,2022,38(3):770-777.
APA Leng, Jiacheng,&Wu, Ling-Yun.(2022).Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs.BIOINFORMATICS,38(3),770-777.
MLA Leng, Jiacheng,et al."Importance-Penalized Joint Graphical Lasso (IPJGL): differential network inference via GGMs".BIOINFORMATICS 38.3(2022):770-777.
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