4.6 Article

A Joint Graphical Model for Inferring Gene Networks Across Multiple Subpopulations and Data Types

期刊

IEEE TRANSACTIONS ON CYBERNETICS
卷 51, 期 2, 页码 1043-1055

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2019.2952711

关键词

Gene network inference; graphical models; group lasso penalty; high-dimensional data; the cancer genome atlas (TCGA)

资金

  1. National Natural Science Foundation of China [11871026, 61402190, 61602309, 61532008]
  2. Natural Science Foundation of Hubei Province [2018CFB521]
  3. Fundamental Research Funds for the Central Universities [CCNU18TS026]
  4. Shenzhen Fundamental Research Program [JCYJ20170817095210760]
  5. Guangdong Basic and Applied Basic Research Foundation [2019A1515011384]
  6. Hong Kong Research Grants Council [C1007-15G, 11200818]

向作者/读者索取更多资源

The study introduces a joint graphical model to estimate multiple gene networks simultaneously, leveraging network decomposition and group lasso penalty to examine similarities and differences among different subpopulations and data types, leading to improved accuracy in gene network reconstruction.
Reconstructing gene networks from gene expression data is a long-standing challenge. In most applications, the observations can be divided into several distinct but related subpopulations and the gene expression measurements can be collected from multiple data types. Most existing methods are designed to estimate a single gene network from a single dataset. These methods may be suboptimal since they do not exploit the similarities and differences among different subpopulations and data types. In this article, we propose a joint graphical model to estimate the multiple gene networks simultaneously. Our model decomposes each subpopulation-specific gene network as a sum of common and unique components and imposes a group lasso penalty on gene networks corresponding to different data types. The gene network variations across subpopulations can be learned automatically by the decompositions of networks, and the similarities and differences among data types can be captured by the group lasso penalty. The simulation studies demonstrate that our method outperforms the state-of-the-art methods. We also apply our method to the cancer genome atlas breast cancer datasets to reconstruct subtype-specific gene networks. Hub nodes in the estimated subnetworks unique to individual cancer subtypes rediscover well-known genes associated with breast cancer subtypes and provide interesting predictions.

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