{"action":"opened","issue":{"url":"https://api.github.com/repos/broadinstitute/gatk-protected/issues/703","repository_url":"https://api.github.com/repos/broadinstitute/gatk-protected","labels_url":"https://api.github.com/repos/broadinstitute/gatk-protected/issues/703/labels{/name}","comments_url":"https://api.github.com/repos/broadinstitute/gatk-protected/issues/703/comments","events_url":"https://api.github.com/repos/broadinstitute/gatk-protected/issues/703/events","html_url":"https://github.com/broadinstitute/gatk-protected/issues/703","id":176105088,"number":703,"title":"Increasing the dimension of the latent space progressively while 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Tools alpha-4 release","description":"#### General\r\n- Automated evaluations for ACNV, germline, etc.\r\n- Refactor and consolidate CNV/ACNV classes\r\n- Optimize WGS performance\r\n- Spark implementation of het pulldown\r\n- Integrate generative coverage model into somatic pipeline\r\n- Evaluate HMM segmentation\r\n- Evaluate and improve purity/ploidy tool\r\n- Integrate somatic SNVs\r\n- Prototype phylogeny tool\r\n\r\n","creator":{"login":"LeeTL1220","id":2152339,"avatar_url":"https://avatars.githubusercontent.com/u/2152339?v=3","gravatar_id":"","url":"https://api.github.com/users/LeeTL1220","html_url":"https://github.com/LeeTL1220","followers_url":"https://api.github.com/users/LeeTL1220/followers","following_url":"https://api.github.com/users/LeeTL1220/following{/other_user}","gists_url":"https://api.github.com/users/LeeTL1220/gists{/gist_id}","starred_url":"https://api.github.com/users/LeeTL1220/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/LeeTL1220/subscriptions","organizations_url":"https://api.github.com/users/LeeTL1220/orgs","repos_url":"https://api.github.com/users/LeeTL1220/repos","events_url":"https://api.github.com/users/LeeTL1220/events{/privacy}","received_events_url":"https://api.github.com/users/LeeTL1220/received_events","type":"User","site_admin":false},"open_issues":55,"closed_issues":2,"state":"open","created_at":"2016-03-02T15:53:05Z","updated_at":"2016-09-09T20:42:41Z","due_on":"2016-12-30T00:00:00Z","closed_at":null},"comments":0,"created_at":"2016-09-09T20:42:41Z","updated_at":"2016-09-09T20:42:41Z","closed_at":null,"body":"The MLE of the coverage model has many local minima. The EM algorithm lands on different solutions depending on how early one starts updating copy ratio posteriors, and \"how\" the dimension of the latent space is set (see below).\r\n\r\nIf one starts with a high dimensional latent space (assuming no CNV-avoiding regularization), most of the real events will be identified as bias and get washed off, such that the HMM will be unable to identify any change points. This leads to low sensitivity. On the other hand, if one starts with a low dimensional latent space, most of the real CNV events will remain intact, allowing the HMM to identify many more change points, leading to high sensitivity (though, low specificity).\r\n\r\nIt is desirable to have both protocols implemented and to check which one gives better solutions. We are really exploring the land of heuristics here. Due to the large scale of the problem, an annealing-based approach is unlikely to be practical (is it?); hence, we have to resort to heuristics."}}
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