{ "_id": "6a2522634b233be1983913bb", "Package": "crso", "Title": "Cancer Rule Set Optimization ('crso')", "Version": "0.1.1", "Author": "Michael Klein ", "Maintainer": "Michael Klein ", "Description": "An algorithm for identifying candidate driver combinations\nin cancer. CRSO is based on a theoretical model of cancer in\nwhich a cancer rule is defined to be a collection of two or\nmore events (i.e., alterations) that are minimally sufficient\nto cause cancer. A cancer rule set is a set of cancer rules\nthat collectively are assumed to account for all of ways to\ncause cancer in the population. In CRSO every event is\ndesignated explicitly as a passenger or driver within each\npatient. Each event is associated with a patient-specific,\nevent-specific passenger penalty, reflecting how unlikely the\nevent would have happened by chance, i.e., as a passenger. CRSO\nevaluates each rule set by assigning all samples to a rule in\nthe rule set, or to the null rule, and then calculating the\ntotal statistical penalty from all unassigned event. CRSO uses\na three phase procedure find the best rule set of fixed size K\nfor a range of Ks. A core rule set is then identified from\namong the best rule sets of size K as the rule set that best\nbalances rule set size and statistical penalty. Users should\nconsult the 'crso' vignette for an example walk through of a\nfull CRSO run. The full description, of the CRSO algorithm is\npresented in: Klein MI, Cannataro V, Townsend J, Stern DF and\nZhao H. \"Identifying combinations of cancer driver in\nindividual patients.\" BioRxiv 674234 [Preprint]. June 19, 2019.\n. 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"uuid": 21145039, "type": "user", "name": "michaelklein916" }, "_downloads": { "count": 34, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/crso" }, "_searchresults": 22, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/crso.html", "manual.pdf" ], "_realowner": "michaelklein916", "_cranurl": false, "_releases": [ { "version": "0.1.1", "date": "2019-07-07" } ], "_exports": [ "buildRuleLibrary", "evaluateListOfIMs", "getBestRsList", "getCoreK", "getCoreRS", "getGCDs", "getGCEs", "getGCRs", "getPoolSizes", "getRulesAsStrings", "makeFilteredImList", "makePhaseOneOrderedRM", "makePhaseThreeImList", "makePhaseTwoImList", "makeSubCoreList" ], "_datasets": [ { "name": "skcm.list", "title": "Example data set derived from TCGA skin cutaneous melanoma (SKCM) data.", "object": "skcm", "class": [ "list" ], "fields": [], "table": false, "tojson": true } ], "_help": [ { "page": 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binary rule matrix as strings", "topics": [ "getRulesAsStrings" ] }, { "page": "makeFilteredImList", "title": "Make filtered im list from phase 3 im list", "topics": [ "makeFilteredImList" ] }, { "page": "makePhaseOneOrderedRM", "title": "Order rules according to phase one importance ranking", "topics": [ "makePhaseOneOrderedRM" ] }, { "page": "makePhaseThreeImList", "title": "Make phase 3 im list from phase 2 im list", "topics": [ "makePhaseThreeImList" ] }, { "page": "makePhaseTwoImList", "title": "Output list of top rule sets for each k in 1:k.max", "topics": [ "makePhaseTwoImList" ] }, { "page": "makeSubCoreList", "title": "Get list of core rules from random subsets of samples", "topics": [ "makeSubCoreList" ] }, { "page": "skcm.list", "title": "Example data set derived from TCGA skin cutaneous melanoma (SKCM) data.", "topics": [ "skcm.list" ] } ], "_rundeps": [ "codetools", "foreach", "iterators" ], "_vignettes": [ { "source": "CRSO-vignette.Rmd", "filename": "CRSO-vignette.html", 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