Microarray analysis often leads to either too large or too small numbers of gene candidates to allow meaningful identifi cation of functional signatures. We aimed at overcoming this hurdle by combining two algorithms: (i) Independent Component Analysis to extract Statistically-based potential signatures. (ii) Gene Set Enrichment Analysis to produce a score of enrichment with statistical significance of each potential signature. We have applied this strategy to identify regulatory T cell (Treg) molecular signatures from two experiments in mice, with cross-validation. These signatures can detect the textasciitilde1% Treg in whole spleen. These fi ndings demonstrate the relevance of our approach as a signature discovery tool.