Journal
IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
Volume 19, Issue 1, Pages 513-521Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TCBB.2020.3002906
Keywords
Gene network rewiring; partial correlation; graphical model; fused lasso
Categories
Funding
- National Natural Science Foundation of China [11871026, 61402190, 61602309, 61532008]
- Natural Science Foundation of Hubei province [2018CFB521]
- Fundamental Research Funds for the Central Universities [CCNU18TS026]
- Shenzhen Research and Development program [JCYJ20170817095210760]
- Natural Science Foundation of SZU [2017077]
- Science and Technology Program of Guangzhou [201607010170]
- Hong Kong Research Grants Council [C1007-15G, 11200818]
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Learning how gene regulatory networks change under different conditions is important. Existing methods for inferring differential networks have limitations. In this study, a new method is proposed and shown to outperform other methods in simulation studies and applications.
It is an important task to learn how gene regulatory networks change under different conditions. Several Gaussian graphical model-based methods have been proposed to deal with this task by inferring differential networks from gene expression data. However, most existing methods define the differential networks as the difference of precision matrices, which may include false differential edges caused by the change of conditional variances. In addition, prior information about the condition-specific networks and the differential networks can be obtained from other domains. It is useful to incorporate prior information into differential network analysis. In this study, we propose a new differential network analysis method to address the above challenges. Instead of using the precision matrices, we define the differential networks as the difference of partial correlations, which can exclude the spurious differential edges due to the variants of conditional variances. Furthermore, prior information from multiple hypothesis testing is incorporated using a weighted fused penalty. Simulation studies show that our method outperforms the competing methods. We also apply our method to identify the differential network between luminal A and basal-like subtypes of breast cancers and the differential network between acute myeloid leukemia tumors and normal samples. The hub genes in the differential networks identified by our method carry out important biological functions.
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