{"action":"created","issue":{"url":"https://api.github.com/repos/taborlab/FlowCal/issues/213","repository_url":"https://api.github.com/repos/taborlab/FlowCal","labels_url":"https://api.github.com/repos/taborlab/FlowCal/issues/213/labels{/name}","comments_url":"https://api.github.com/repos/taborlab/FlowCal/issues/213/comments","events_url":"https://api.github.com/repos/taborlab/FlowCal/issues/213/events","html_url":"https://github.com/taborlab/FlowCal/pull/213","id":158060940,"number":213,"title":"MEFing for floating-point data","user":{"login":"castillohair","id":1529481,"avatar_url":"https://avatars.githubusercontent.com/u/1529481?v=3","gravatar_id":"","url":"https://api.github.com/users/castillohair","html_url":"https://github.com/castillohair","followers_url":"https://api.github.com/users/castillohair/followers","following_url":"https://api.github.com/users/castillohair/following{/other_user}","gists_url":"https://api.github.com/users/castillohair/gists{/gist_id}","starred_url":"https://api.github.com/users/castillohair/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/castillohair/subscriptions","organizations_url":"https://api.github.com/users/castillohair/orgs","repos_url":"https://api.github.com/users/castillohair/repos","events_url":"https://api.github.com/users/castillohair/events{/privacy}","received_events_url":"https://api.github.com/users/castillohair/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":5,"created_at":"2016-06-02T05:15:11Z","updated_at":"2016-06-17T19:47:30Z","closed_at":null,"pull_request":{"url":"https://api.github.com/repos/taborlab/FlowCal/pulls/213","html_url":"https://github.com/taborlab/FlowCal/pull/213","diff_url":"https://github.com/taborlab/FlowCal/pull/213.diff","patch_url":"https://github.com/taborlab/FlowCal/pull/213.patch"},"body":"Solves #195.\r\n\r\n- MEFing of data acquired with linear amplification is supported, both with the Excel UI and Python API. Changes required to support this include new or modified functions in `FlowCal.mef`, a new standard curve model, and the fact that all data should now be transformed manually to a.u. before MEF.\r\n- Floating-point data with negative values is correctly processed. In particular, logicle scale has been implemented for visualization, and it is the default (Excel UI) for data acquired with linear amplification. Geometric statistics are calculated on the positive events, and a warning is thrown indicating the percentage of valid events.\r\n- Tested with examples and private dataset aquired using the SEA's flow cytometer. The latter exports FCS files version 2.0 (integer) with either linear or log amplification, and FCS3.0 (floating-point, negative values) with linear amplification (log amplification with floating point is not allowed by the standard). MEFing with the Excel UI seems to be performed correctly, as far as I can tell, with these three datasets."},"comment":{"url":"https://api.github.com/repos/taborlab/FlowCal/issues/comments/226864963","html_url":"https://github.com/taborlab/FlowCal/pull/213#issuecomment-226864963","issue_url":"https://api.github.com/repos/taborlab/FlowCal/issues/213","id":226864963,"user":{"login":"castillohair","id":1529481,"avatar_url":"https://avatars.githubusercontent.com/u/1529481?v=3","gravatar_id":"","url":"https://api.github.com/users/castillohair","html_url":"https://github.com/castillohair","followers_url":"https://api.github.com/users/castillohair/followers","following_url":"https://api.github.com/users/castillohair/following{/other_user}","gists_url":"https://api.github.com/users/castillohair/gists{/gist_id}","starred_url":"https://api.github.com/users/castillohair/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/castillohair/subscriptions","organizations_url":"https://api.github.com/users/castillohair/orgs","repos_url":"https://api.github.com/users/castillohair/repos","events_url":"https://api.github.com/users/castillohair/events{/privacy}","received_events_url":"https://api.github.com/users/castillohair/received_events","type":"User","site_admin":false},"created_at":"2016-06-17T19:47:30Z","updated_at":"2016-06-17T19:47:30Z","body":"So the issues in which I'd still like feedback are the following:\r\n\r\n- Model's equations, particularly about the way we're applying the standard curve to negative events.\r\n- Using `hist_bins(..., nbins=1)` in cases where actually using the `range` is to be expected. As I said, using the range directly is problematic for log scale because if the lower limit is zero, its logarithm is undefined. `hist_bins` takes care of this automatically, because it gets knowledge of the relevant scale via its `scale` parameter. But perhaps there is a better way to do this.\r\n- The scales used in the Excel UI and default scales in functions.\r\n\r\nI'm gonna try to do `logicle` in `mef.clustering_gmm` and `mef.selection_std`, and see how that looks."}}
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