The identification of condition-specific gene sets from transcriptomic
experiments is important to reveal regulatory and signaling mechanisms
associated with a given cellular response. Statistical methods of
differential expression analysis, designed to assess individual gene
variations, have trouble highlighting modules of small varying genes whose
interaction is essential to characterize phenotypic changes. To identify
these highly informative gene modules, several methods have been proposed in
recent years, but they have many limitations that make them of little use to
biologists. Here, we propose an efficient method for identifying these
active modules that operates on a data embedding combining gene expressions
and interaction data. Applications carried out on real datasets show that
our method can identify new groups of genes of high interest corresponding
to functions not revealed by traditional approaches. Software is available
athttps://github.com/claudepasquier/amine.