{"action":"opened","issue":{"url":"https://api.github.com/repos/KrishnaswamyLab/MAGIC/issues/108","repository_url":"https://api.github.com/repos/KrishnaswamyLab/MAGIC","labels_url":"https://api.github.com/repos/KrishnaswamyLab/MAGIC/issues/108/labels{/name}","comments_url":"https://api.github.com/repos/KrishnaswamyLab/MAGIC/issues/108/comments","events_url":"https://api.github.com/repos/KrishnaswamyLab/MAGIC/issues/108/events","html_url":"https://github.com/KrishnaswamyLab/MAGIC/issues/108","id":335579713,"node_id":"MDU6SXNzdWUzMzU1Nzk3MTM=","number":108,"title":"Statistically significant gene modules?","user":{"login":"andbsp","id":40581547,"node_id":"MDQ6VXNlcjQwNTgxNTQ3","avatar_url":"https://avatars0.githubusercontent.com/u/40581547?v=4","gravatar_id":"","url":"https://api.github.com/users/andbsp","html_url":"https://github.com/andbsp","followers_url":"https://api.github.com/users/andbsp/followers","following_url":"https://api.github.com/users/andbsp/following{/other_user}","gists_url":"https://api.github.com/users/andbsp/gists{/gist_id}","starred_url":"https://api.github.com/users/andbsp/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/andbsp/subscriptions","organizations_url":"https://api.github.com/users/andbsp/orgs","repos_url":"https://api.github.com/users/andbsp/repos","events_url":"https://api.github.com/users/andbsp/events{/privacy}","received_events_url":"https://api.github.com/users/andbsp/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2018-06-25T21:46:12Z","updated_at":"2018-06-25T21:46:12Z","closed_at":null,"author_association":"NONE","body":"I was wondering if there are any suggestions for calculating \"statistically significant\" gene modules (i.e. genes that are more correlated than you would expect by chance)? My general approach would be:\r\n\r\n1) Calculate correlation coefficients between genes\r\n2) Shuffle data to remove all true gene-gene correlations\r\n3) Estimate null distribution of correlation coefficients using shuffled data\r\n4) Find statistically significant modules, where the true correlations are greater than the null model\r\n\r\nHowever, the problem I'm having is that even after I shuffle the data, MAGIC induces many artificial correlations. So I'm not sure how to assess whether a correlation is statistically significant or not. Any ideas?"}}
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