Document Detail

Gene regulatory network discovery using pairwise Granger causality.
MedLine Citation:
PMID:  24067420     Owner:  NLM     Status:  In-Data-Review    
Discovery of gene regulatory network from gene expression data can yield a useful insight to drug development. Among the methods applied to time-series data, Granger causality (GC) has emerged as a powerful tool with several merits. Since gene expression data usually have a much larger number of genes than time points therefore a full model cannot be applied in a straightforward manner, GC is often applied to genes pairwisely. In this study, the authors first investigate with synthetic data how spurious causalities (false discoveries) may arise because of the use of pairwise rather than full-model GC detection. Furthermore, spurious causalities may also arise if the order of the vector autoregressive model is not high enough. As a remedy, the authors demonstrate that model validation techniques can effectively reduce the number of false discoveries. Then, they apply pairwise GC with model validation to the real human HeLa cell-cycle dataset. They find that Akaike information criterion is generally most suitable for determining model order, but precaution should be taken for extremely short time series. With the authors proposed implementation, degree distributions and network hubs are obtained and compared with existing results, giving a new observation that the hubs tend to act as sources rather than receivers of interactions.
Gary Hak Fui Tam; Chunqi Chang; Yeung Sam Hung
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  IET systems biology     Volume:  7     ISSN:  1751-8849     ISO Abbreviation:  IET Syst Biol     Publication Date:  2013 Oct 
Date Detail:
Created Date:  2013-09-26     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101301198     Medline TA:  IET Syst Biol     Country:  England    
Other Details:
Languages:  eng     Pagination:  195-204     Citation Subset:  IM    
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