Journal article
Identifying Gene Network Rewiring Using Robust Differential Graphical Model with Multivariate t-Distribution
IEEE/ACM transactions on computational biology and bioinformatics, v 17(2), pp 712-718
01 Mar 2020
PMID: 30802872
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Identifying gene network rewiring under different biological conditions is important for understanding the mechanisms underlying complex diseases. Gaussian graphical models, which assume the data follow the multivariate normal distribution, are widely used to identify gene network rewiring. However, the normality assume often fails in reality since the data are contaminated by extreme outliers in general. In this study, we propose a new robust differential graphical model to identify gene network rewiring between two conditions based on the multivariate t-distribution. The multivariate t-distribution is more robust to outliers than the normal distribution since it has heavy tails and allows values far from the mean. A fused lasso penalty is used to borrow information across conditions to improve the results. We develop an expectation maximization algorithm to solve the optimization model. Experiment results on simulated data show that our method outperforms the state-of-the-art methods. Our method is also applied to identify gene network rewiring between luminal A and basal-like subtypes of breast cancer, and gene network rewiring between the proneural and mesenchymal subtypes of glioblastoma. Several key genes which drive gene network rewiring are discovered.
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Details
- Title
- Identifying Gene Network Rewiring Using Robust Differential Graphical Model with Multivariate t-Distribution
- Creators
- Rui Yuan - Central China Normal UniversityLe Ou-Yang - Shenzhen UniversityXiaohua Hu - Drexel UniversityXiao-Fei Zhang - Central China Normal University
- Publication Details
- IEEE/ACM transactions on computational biology and bioinformatics, v 17(2), pp 712-718
- Publisher
- IEEE
- Number of pages
- 7
- Grant note
- 11871026; 61402190; 61602309; 61532008 / National Natural Science Foundation of China; National Natural Science Foundation of China (NSFC) CCNU18TS026 / Self-Determined Research Funds of CCNU from the Colleges Basic Research and Operation of MOE 2018CFB521 / Natural Science Foundation of Hubei province; Natural Science Foundation of Hubei Province 2017077 / Natural Science Foundation of SZU JCYJ20170817095210760 / Shenzhen Research and Development program
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000524236800030
- Scopus ID
- 2-s2.0-85083027686
- Other Identifier
- 991019167808104721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemical Research Methods
- Computer Science, Interdisciplinary Applications
- Mathematics, Interdisciplinary Applications
- Statistics & Probability