{ "_id": "6ad5bac47f75c4fbd3a1e304", "Package": "iPath", "Type": "Package", "Title": "iPath pipeline for detecting perturbed pathways at individual\nlevel", "Version": "1.19.0", "Authors@R": "c(person(\"Kenong\",\"Su\", email =\"kenong.su@emory.edu\", role = c(\"aut\",\"cre\")),\nperson(\"Zhaohui\",\"Qin\", email =\"zhaohui.qin@emory.edu\", role =\"aut\"))", "Description": "iPath is the Bioconductor package used for calculating\npersonalized pathway score and test the association with\nsurvival outcomes. Abundant single-gene biomarkers have been\nidentified and used in the clinics. However, hundreds of\noncogenes or tumor-suppressor genes are involved during the\nprocess of tumorigenesis. We believe individual-level\nexpression patterns of pre-defined pathways or gene sets are\nbetter biomarkers than single genes. In this study, we devised\na computational method named iPath to identify prognostic\nbiomarker pathways, one sample at a time. To test its utility,\nwe conducted a pan-cancer analysis across 14 cancer types from\nThe Cancer Genome Atlas and demonstrated that iPath is capable\nof identifying highly predictive biomarkers for clinical\noutcomes, including overall survival, tumor subtypes, and tumor\nstage classifications. 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