{"doi":"10.1093/bioinformatics/btn577","title":"A novel signaling pathway impact analysis","abstract":"<jats:title>Abstract</jats:title>\n               <jats:p>Motivation: Gene expression class comparison studies may identify hundreds or thousands of genes as differentially expressed (DE) between sample groups. Gaining biological insight from the result of such experiments can be approached, for instance, by identifying the signaling pathways impacted by the observed changes. Most of the existing pathway analysis methods focus on either the number of DE genes observed in a given pathway (enrichment analysis methods), or on the correlation between the pathway genes and the class of the samples (functional class scoring methods). Both approaches treat the pathways as simple sets of genes, disregarding the complex gene interactions that these pathways are built to describe.</jats:p>\n               <jats:p>Results: We describe a novel signaling pathway impact analysis (SPIA) that combines the evidence obtained from the classical enrichment analysis with a novel type of evidence, which measures the actual perturbation on a given pathway under a given condition. A bootstrap procedure is used to assess the significance of the observed total pathway perturbation. Using simulations we show that the evidence derived from perturbations is independent of the pathway enrichment evidence. This allows us to calculate a global pathway significance P-value, which combines the enrichment and perturbation P-values. We illustrate the capabilities of the novel method on four real datasets. The results obtained on these data show that SPIA has better specificity and more sensitivity than several widely used pathway analysis methods.</jats:p>\n               <jats:p>Availability: SPIA was implemented as an R package available at http://vortex.cs.wayne.edu/ontoexpress/</jats:p>\n               <jats:p>Contact:  sorin@wayne.edu</jats:p>\n               <jats:p>Supplementary information:  Supplementary data are available at Bioinformatics online.</jats:p>","journal":"Bioinformatics","year":2009,"id":619351,"datarank":1.0485384750740823,"base_score":6.990256500493881,"endowment":6.990256500493881,"self_citation_contribution":1.0485384750740823,"citation_network_contribution":0.0,"self_endowment_contribution":1.0485384750740823,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":1085,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":12306,"name":"Sorin Draghici","orcid":null,"position":1,"is_corresponding":false},{"id":4851,"name":"Purvesh Khatri","orcid":"0000-0002-4143-4708","position":2,"is_corresponding":false},{"id":557040,"name":"Sonia S. Hassan","orcid":"0000-0002-9306-3961","position":3,"is_corresponding":false},{"id":1288802,"name":"Pooja Mittal","orcid":"0000-0002-2156-1419","position":4,"is_corresponding":false},{"id":1598262,"name":"Jung-sun Kim","orcid":null,"position":5,"is_corresponding":false},{"id":240941,"name":"Chong Jai Kim","orcid":"0000-0002-2844-9446","position":6,"is_corresponding":false},{"id":655560,"name":"Juan Pedro Kusanovic","orcid":"0000-0003-0640-2070","position":7,"is_corresponding":false},{"id":236857,"name":"Roberto Romero","orcid":"0000-0002-4448-5121","position":8,"is_corresponding":false},{"id":1598260,"name":"Adi Laurentiu Tarca","orcid":null,"position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"A novel signaling pathway impact analysis","abstract":"<jats:title>Abstract</jats:title>\n               <jats:p>Motivation: Gene expression class comparison studies may identify hundreds or thousands of genes as differentially expressed (DE) between sample groups. Gaining biological insight from the result of such experiments can be approached, for instance, by identifying the signaling pathways impacted by the observed changes. Most of the existing pathway analysis methods focus on either the number of DE genes observed in a given pathway (enrichment analysis methods), or on the correlation between the pathway genes and the class of the samples (functional class scoring methods). Both approaches treat the pathways as simple sets of genes, disregarding the complex gene interactions that these pathways are built to describe.</jats:p>\n               <jats:p>Results: We describe a novel signaling pathway impact analysis (SPIA) that combines the evidence obtained from the classical enrichment analysis with a novel type of evidence, which measures the actual perturbation on a given pathway under a given condition. A bootstrap procedure is used to assess the significance of the observed total pathway perturbation. Using simulations we show that the evidence derived from perturbations is independent of the pathway enrichment evidence. This allows us to calculate a global pathway significance P-value, which combines the enrichment and perturbation P-values. We illustrate the capabilities of the novel method on four real datasets. The results obtained on these data show that SPIA has better specificity and more sensitivity than several widely used pathway analysis methods.</jats:p>\n               <jats:p>Availability: SPIA was implemented as an R package available at http://vortex.cs.wayne.edu/ontoexpress/</jats:p>\n               <jats:p>Contact:  sorin@wayne.edu</jats:p>\n               <jats:p>Supplementary information:  Supplementary data are available at Bioinformatics online.</jats:p>","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"18990722","pmcid":"PMC2732297","openalex_id":null,"authors":[],"funders":[{"funder_name":"NCI NIH HHS","grant_id":"1U01CA117478","title":null},{"funder_name":"NIBIB NIH HHS","grant_id":"5R21EB000990","title":null},{"funder_name":"NINDS NIH HHS","grant_id":"1R01 NS045207","title":null},{"funder_name":"NHGRI NIH HHS","grant_id":"1R01HG003491","title":null},{"funder_name":"NCI NIH HHS","grant_id":"2P30 CA022453","title":null},{"funder_name":"NCI NIH HHS","grant_id":"1R21CA100740","title":null},{"funder_name":"Intramural NIH HHS","grant_id":"","title":null}],"total_grants":7,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"bronze","license":null,"oa_locations":[{"url":"https://academic.oup.com/bioinformatics/article-pdf/25/1/75/48982697/bioinformatics_25_1_75.pdf","host_type":"publisher"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/2732297","host_type":"repository"}],"fields_of_study":[],"mesh_terms":["Humans","Colorectal Neoplasms","False Positive Reactions","Computational Biology","Signal Transduction","Pregnancy","Labor, Obstetric","Models, Genetic","Databases, Genetic","Female"],"keywords":[],"sdg_mappings":[],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"geo"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-03T06:51:11.140600Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}