title: Using pathway signatures as means of identifying similarities among microarray experiments.
abstract: Widespread use of microarrays has generated large amounts of data, the interrogation of the public microarray repositories, identifying similarities between microarray experiments is now one of the major challenges. Approaches using defined group of genes, such as pathways and cellular networks (pathway analysis), have been proposed to improve the interpretation of microarray experiments. We propose a novel method to compare microarray experiments at the pathway level, this method consists of two steps: first, generate pathway signatures, a set of descriptors recapitulating the biologically meaningful pathways related to some clinical/biological variable of interest, second, use these signatures to interrogate microarray databases. We demonstrate that our approach provides more reliable results than with gene-based approaches. While gene-based approaches tend to suffer from bias generated by the analytical procedures employed, our pathway based method successfully groups together similar samples, independently of the experimental design. The results presented are potentially of great interest to improve the ability to query and compare experiments in public repositories of microarray data. As a matter of fact, this method can be used to retrieve data from public microarray databases and perform comparisons at the pathway level.
authors.affiliation: Department of Pharmacology, University of Firenze, Firenze, Italy.
authors.forename: Luca
authors.initials: L
authors.lastname: Beltrame
authors.forename: Lisa
authors.initials: L
authors.lastname: Rizzetto
authors.forename: Raffaele
authors.initials: R
authors.lastname: Paola
authors.forename: Philippe
authors.initials: P
authors.lastname: Rocca-Serra
authors.forename: Luca
authors.initials: L
authors.lastname: Gambineri
authors.forename: Cristina
authors.initials: C
authors.lastname: Battaglia
authors.forename: Duccio
authors.initials: D
authors.lastname: Cavalieri
mesh_terms: D016000:Cluster Analysis; D030541:Databases, Genetic; D003713:Dendritic Cells / Q000502:physiology; D020869:Gene Expression Profiling / Q000379:methods*; D053263:Gene Regulatory Networks; D005800:Genes, Fungal; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012441:Saccharomyces cerevisiae / Q000235:genetics; D015398:Signal Transduction