title: Knowledge based identification of essential signaling from genome-scale siRNA experiments.
abstract: BACKGROUND
  A systems biology interpretation of genome-scale RNA interference (RNAi) experiments is complicated by scope, experimental variability and network signaling robustness. Over representation approaches (ORA), such as the Hypergeometric or z-score, are an established statistical framework used to associate RNA interference effectors to biologically annotated gene sets or pathways. These methods, however, do not directly take advantage of our growing understanding of the interactome. Furthermore, these methods can miss partial pathway activation and may be biased by protein complexes. Here we present a novel ORA, protein interaction permutation analysis (PIPA), that takes advantage of canonical pathways and established protein interactions to identify pathways enriched for protein interactions connecting RNAi hits.
  
  
  RESULTS
  We use PIPA to analyze genome-scale siRNA cell growth screens performed in HeLa and TOV cell lines. First we show that interacting gene pair siRNA hits are more reproducible than single gene hits. Using protein interactions, PIPA identifies enriched pathways not found using the standard Hypergeometric analysis including the FAK cytoskeletal remodeling pathway. Different branches of the FAK pathway are distinctly essential in HeLa versus TOV cell lines while other portions are uneffected by siRNA perturbations. Enriched hits belong to protein interactions associated with cell cycle regulation, anti-apoptosis, and signal transduction.
  
  
  CONCLUSIONS
  PIPA provides an analytical framework to interpret siRNA screen data by merging biologically annotated gene sets with the human interactome. As a result we identify pathways and signaling hypotheses that are statistically enriched to effect cell growth in human cell lines. This method provides a complementary approach to standard gene set enrichment that utilizes the additional knowledge of specific interactions within biological gene sets.
authors: Rosetta Inpharmatics LLC, a wholly owned subsidiary of Merck & Co,, Inc, Seattle, WA 98109, USA. armand.bankhead@gmail.com
authors: Armand
authors: A
authors: Bankhead
authors: Iliana
authors: I
authors: Sach
authors: Chester
authors: C
authors: Ni
authors: Nolwenn
authors: N
authors: LeMeur
authors: Mark
authors: M
authors: Kruger
authors: Marc
authors: M
authors: Ferrer
authors: Robert
authors: R
authors: Gentleman
authors: Carol
authors: C
authors: Rohl
mesh_terms: D000465:Algorithms; D002460:Cell Line; D015870:Gene Expression; D015894:Genome, Human; D006801:Humans; D011485:Protein Binding; D011506:Proteins / Q000235:genetics*; D034741:RNA, Small Interfering / Q000235:genetics*; D015398:Signal Transduction; D049490:Systems Biology
chemical_list: D011506:Proteins; D034741:RNA, Small Interfering