title: A novel strategy for molecular signature discovery based on independent component analysis.
abstract: Microarray analysis often leads to either too large or too small numbers of gene candidates to allow meaningful identification 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 -1% Treg in whole spleen. These findings demonstrate the relevance of our approach as a signature discovery tool.
authors.forename: Hang-Phuong
authors.initials: HP
authors.lastname: Pham
authors.forename: Nicolas
authors.initials: N
authors.lastname: Dérian
authors.forename: Wahiba
authors.initials: W
authors.lastname: Chaara
authors.forename: Bertrand
authors.initials: B
authors.lastname: Bellier
authors.forename: David
authors.initials: D
authors.lastname: Klatzmann
authors.forename: Adrien
authors.initials: A
authors.lastname: Six
mesh_terms: D000256:Adenoviridae / Q000378:metabolism; D062705:Adenovirus Vaccines / Q000378:metabolism; D000465:Algorithms; D000818:Animals; D019295:Computational Biology / Q000379:methods*; D057225:Data Mining; D005260:Female; D051379:Mice; D008810:Mice, Inbred C57BL; D020411:Oligonucleotide Array Sequence Analysis; D012984:Software; D013154:Spleen / Q000378:metabolism; D050378:T-Lymphocytes, Regulatory / Q000166:cytology
chemical_list: D062705:Adenovirus Vaccines