{"action":"created","issue":{"url":"https://api.github.com/repos/Bioconductor/Contributions/issues/225","repository_url":"https://api.github.com/repos/Bioconductor/Contributions","labels_url":"https://api.github.com/repos/Bioconductor/Contributions/issues/225/labels{/name}","comments_url":"https://api.github.com/repos/Bioconductor/Contributions/issues/225/comments","events_url":"https://api.github.com/repos/Bioconductor/Contributions/issues/225/events","html_url":"https://github.com/Bioconductor/Contributions/issues/225","id":197374194,"number":225,"title":"MSPC","user":{"login":"julaiti","id":16766975,"avatar_url":"https://avatars.githubusercontent.com/u/16766975?v=3","gravatar_id":"","url":"https://api.github.com/users/julaiti","html_url":"https://github.com/julaiti","followers_url":"https://api.github.com/users/julaiti/followers","following_url":"https://api.github.com/users/julaiti/following{/other_user}","gists_url":"https://api.github.com/users/julaiti/gists{/gist_id}","starred_url":"https://api.github.com/users/julaiti/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/julaiti/subscriptions","organizations_url":"https://api.github.com/users/julaiti/orgs","repos_url":"https://api.github.com/users/julaiti/repos","events_url":"https://api.github.com/users/julaiti/events{/privacy}","received_events_url":"https://api.github.com/users/julaiti/received_events","type":"User","site_admin":false},"labels":[{"id":335833554,"url":"https://api.github.com/repos/Bioconductor/Contributions/labels/2.%20review%20in%20progress","name":"2. 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the following URL to point to the GitHub repository of\r\nthe package you wish to submit to _Bioconductor_\r\n\r\n- Repository: https://github.com/julaiti/MSPC\r\n\r\nConfirm the following by editing each check box to '[x]'\r\n\r\n- [x] I understand that by submitting my package to _Bioconductor_,\r\n  the package source and all review commentary are visible to the\r\n  general public.\r\n\r\n- [x] I have read the _Bioconductor_ [Package Submission][2]\r\n  instructions. My package is consistent with the _Bioconductor_\r\n  [Package Guidelines][1].\r\n\r\n- [x] I understand that a minimum requirement for package acceptance \r\n  is to pass R CMD check and R CMD BiocCheck with no ERROR or WARNINGS. \r\n  Passing these checks does not result in automatic acceptance. The \r\n  package will then undergo a formal review and recommendations for \r\n  acceptance regarding other Bioconductor standards will be addressed.\r\n\r\n- [x] My package addresses statistical or bioinformatic issues related\r\n  to the analysis and comprehension of high throughput genomic data.\r\n\r\n- [x] I am committed to the long-term maintenance of my package. This\r\n  includes monitoring the [support site][3] for issues that users may\r\n  have, subscribing to the [bioc-devel][4] mailing list to stay aware\r\n  of developments in the _Bioconductor_ community, responding promptly\r\n  to requests for updates from the Core team in response to changes in\r\n  _R_ or underlying software.\r\n\r\nI am familiar with the essential aspects of _Bioconductor_ software\r\nmanagement, including:\r\n\r\n- [x] The 'devel' branch for new packages and features.\r\n- [x] The stable 'release' branch, made available every six\r\n      months, for bug fixes.\r\n- [x] _Bioconductor_ version control using [Subversion][5]\r\n  (optionally [via GitHub][6]).\r\n\r\nFor help with submitting your package, please subscribe and post questions\r\nto the [bioc-devel][4] mailing list.\r\n\r\n[1]: https://bioconductor.org/developers/package-guidelines/\r\n[2]: https://bioconductor.org/developers/package-submission/\r\n[3]: https://support.bioconductor.org\r\n[4]: https://stat.ethz.ch/mailman/listinfo/bioc-devel\r\n[5]: http://bioconductor.org/developers/how-to/source-control/\r\n[6]: http://bioconductor.org/developers/how-to/git-mirrors/\r\n"},"comment":{"url":"https://api.github.com/repos/Bioconductor/Contributions/issues/comments/273226436","html_url":"https://github.com/Bioconductor/Contributions/issues/225#issuecomment-273226436","issue_url":"https://api.github.com/repos/Bioconductor/Contributions/issues/225","id":273226436,"user":{"login":"mtmorgan","id":118552,"avatar_url":"https://avatars.githubusercontent.com/u/118552?v=3","gravatar_id":"","url":"https://api.github.com/users/mtmorgan","html_url":"https://github.com/mtmorgan","followers_url":"https://api.github.com/users/mtmorgan/followers","following_url":"https://api.github.com/users/mtmorgan/following{/other_user}","gists_url":"https://api.github.com/users/mtmorgan/gists{/gist_id}","starred_url":"https://api.github.com/users/mtmorgan/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/mtmorgan/subscriptions","organizations_url":"https://api.github.com/users/mtmorgan/orgs","repos_url":"https://api.github.com/users/mtmorgan/repos","events_url":"https://api.github.com/users/mtmorgan/events{/privacy}","received_events_url":"https://api.github.com/users/mtmorgan/received_events","type":"User","site_admin":false},"created_at":"2017-01-17T16:49:33Z","updated_at":"2017-01-17T16:49:33Z","body":"If `peakOverlapping()` has an argument `type=c(\"max\", \"min\")` then\r\n\r\n    type = match.arg(type)\r\n    \r\ndefaults to `max` but can also have value `min`. Then `fromLast=(type == \"max\")` selects the maximum value (last, i.e., largest, ordered score for each query / subject combination) when the argument is `max`, and the minimum value (first, i.e., smallest, ordered score for each query / subject combination) when the argument is `min`.\r\n\r\nThe query samples corresponding to overlapping hits are something like\r\n\r\n    sampleIdx <- factor(\r\n        rep(names(peakset), each=lengths(peakset)),\r\n        levels=names(peakset)\r\n    )\r\n    querySample <- sampleIdx[mcols$query]\r\n    \r\nand similarly for `subjectSample`. It seems like you are wanting something like\r\n\r\n    result <- lapply(levels(sampleIdx), function(sample) {\r\n        idx <- (querySample == sample) &   # overlapping focal sample\r\n            (querySample != subjectSample)  # not self-hits\r\n        vapply(split(mcols$p.value, idx), function(...) {\r\n            # calculate p value for group\r\n        })\r\n    })\r\n    # ?? represent data, maybe as DataFrame with mcols$query and unlist(pvalue)\r\n    \r\nSome of the p value computation might be'hoisted' out of one or both loops, e.g., `lg.pv`, `pchisq()`, and similar.\r\n  \r\n\r\n"}}
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