Send to

Choose Destination
See comment in PubMed Commons below
Nucleic Acids Res. 2014 Apr;42(7):e60. doi: 10.1093/nar/gku099. Epub 2014 Feb 5.

EDDY: a novel statistical gene set test method to detect differential genetic dependencies.

Author information

Integrated Cancer Genomics Division, Biocomputing Unit, Translational Genomics Research Institute, 445 North 5th Street, Phoenix, AZ 85004, USA.


Identifying differential features between conditions is a popular approach to understanding molecular features and their mechanisms underlying a biological process of particular interest. Although many tests for identifying differential expression of gene or gene sets have been proposed, there was limited success in developing methods for differential interactions of genes between conditions because of its computational complexity. We present a method for Evaluation of Dependency DifferentialitY (EDDY), which is a statistical test for differential dependencies of a set of genes between two conditions. Unlike previous methods focused on differential expression of individual genes or correlation changes of individual gene-gene interactions, EDDY compares two conditions by evaluating the probability distributions of dependency networks from genes. The method has been evaluated and compared with other methods through simulation studies, and application to glioblastoma multiforme data resulted in informative cancer and glioblastoma multiforme subtype-related findings. The comparison with Gene Set Enrichment Analysis, a differential expression-based method, revealed that EDDY identifies the gene sets that are complementary to those identified by Gene Set Enrichment Analysis. EDDY also showed much lower false positives than Gene Set Co-expression Analysis, a method based on correlation changes of individual gene-gene interactions, thus providing more informative results. The Java implementation of the algorithm is freely available to noncommercial users. Download from:

[Indexed for MEDLINE]
Free PMC Article
PubMed Commons home

PubMed Commons

How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Silverchair Information Systems Icon for PubMed Central
    Loading ...
    Support Center