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. The R package is available at https://cran.r-project.org/web/packages/DNLC.
journal_name
BMC Bioinformaticsjournal_title
BMC bioinformaticsauthors
Lu J,Lu Y,Ding Y,Xiao Q,Liu L,Cai Q,Kong Y,Bai Y,Yu Tdoi
10.1186/s12859-019-3046-4subject
Has Abstractpub_date
2019-12-24 00:00:00pages
489issue
Suppl 15issn
1471-2105pii
10.1186/s12859-019-3046-4journal_volume
20pub_type
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