DNLC: differential network local consistency analysis

• Type of Material: Article
• Authors: Jianwei Lu, Tongji University; Yao Lu, Tongji University; Yusheng Ding, Tongji University; Qingyang Xiao, Emory University; Linqing Liu, Tongji University; Qingpo Cai, Emory University; Yunchuan Kong, Emory University; Yun Bai, Philadelphia College of Osteopathic Medicine; Tianwei Yu, Emory University
• Language: English
• Date: 2019-12-24
• Publisher: BMC (part of Springer Nature)
• Publication Version: Final Published Version
• Copyright Statement: © 2019 The Author(s).
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1186/s12859-019-3046-4
• Title of Journal or Parent Work: BMC Bioinformatics
• ISSN: 1471-2105
• Volume: 20
• Issue: Suppl 15
• Start Page: 489
• End Page: 489
• Grant/Funding Information: This work was partially supported by NIH grants R01GM124061 and R15GM113120; Ministry of Science and Technology of China 973 Program grant No. 2013CB967101; Natural Science Foundation of China grants No. 41476120; No. 61572362, No. 81571347 and No. 21477087; and Shanghai Science Committee Foundation Grant No. 13PJ1433200.; Publication costs are funded by NIH grant R01GM124061.
• Keywords: ACUTE MYELOID-LEUKEMIA; Local Moran's I; RESISTANCE; LYMPHOBLASTIC-LEUKEMIA; CELL LEUKEMIA; INDUCTION; ACUTE LYMPHOCYTIC-LEUKEMIA; Life Sciences & Biomedicine; Biotechnology & Applied Microbiology; Gene expression; GENE-EXPRESSION; PHOSPHATASE SHIP2; Biological network; Biochemical Research Methods; Biochemistry & Molecular Biology; Mathematical & Computational Biology; CHEMOTHERAPY; METHOTREXATE; Science & Technology
• Research Categories: Biology, Biostatistics; Environmental Sciences

Abstract

Background: The biological network is highly dynamic. Functional relations between genes can be activated or deactivated depending on the biological conditions. On the genome-scale network, subnetworks that gain or lose local expression consistency may shed light on the regulatory mechanisms related to the changing biological conditions, such as disease status or tissue developmental stages. Results: In this study, we develop a new method to select genes and modules on the existing biological network, in which local expression consistency changes significantly between clinical conditions. The method is called DNLC: Differential Network Local Consistency. In simulations, our algorithm detected artificially created local consistency changes effectively. We applied the method on two publicly available datasets, and the method detected novel genes and network modules that were biologically plausible. Conclusions: The new method is effective in finding modules in which the gene expression consistency change between clinical conditions. It is a useful tool that complements traditional differential expression analyses to make discoveries from gene expression data.