{"action":"closed","issue":{"url":"https://api.github.com/repos/bioFAM/MOFA/issues/13","repository_url":"https://api.github.com/repos/bioFAM/MOFA","labels_url":"https://api.github.com/repos/bioFAM/MOFA/issues/13/labels{/name}","comments_url":"https://api.github.com/repos/bioFAM/MOFA/issues/13/comments","events_url":"https://api.github.com/repos/bioFAM/MOFA/issues/13/events","html_url":"https://github.com/bioFAM/MOFA/issues/13","id":308591545,"node_id":"MDU6SXNzdWUzMDg1OTE1NDU=","number":13,"title":"Subsetting Data Prior to Running MOFA","user":{"login":"whitleyo","id":25909144,"node_id":"MDQ6VXNlcjI1OTA5MTQ0","avatar_url":"https://avatars2.githubusercontent.com/u/25909144?v=4","gravatar_id":"","url":"https://api.github.com/users/whitleyo","html_url":"https://github.com/whitleyo","followers_url":"https://api.github.com/users/whitleyo/followers","following_url":"https://api.github.com/users/whitleyo/following{/other_user}","gists_url":"https://api.github.com/users/whitleyo/gists{/gist_id}","starred_url":"https://api.github.com/users/whitleyo/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/whitleyo/subscriptions","organizations_url":"https://api.github.com/users/whitleyo/orgs","repos_url":"https://api.github.com/users/whitleyo/repos","events_url":"https://api.github.com/users/whitleyo/events{/privacy}","received_events_url":"https://api.github.com/users/whitleyo/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":3,"created_at":"2018-03-26T14:16:49Z","updated_at":"2018-06-21T14:56:18Z","closed_at":"2018-06-21T14:56:18Z","author_association":"NONE","body":"Hi,\r\n\r\nI noticed in your paper that you subsetted for DNA methylation probes from the illumina array with the top 1% of variance, as well as the RNA-seq genes with the top 20% of variance across samples. On my own I found out that if I didn't do this, the factors found would be dominated by DNA methylation for my own data, the details of which are discussed in the next paragraph. I'm wondering why the subsetting is required for the MOFA model to identify factors explaining variance in both datatypes.\r\n\r\n\r\n When I initially tried running MOFA on the dataset I have (~50 samples, Illumina EPIC methylation array, RNA-seq), I didn't do this subsetting, and I ended up getting 2 factors explaining about 50% of the DNA methylation data's variance and virtually none of the RNA-seq data's variance. I reran MOFA, but with the same data matrices subsetted for the top 20% variant genes for RNA-seq and top 1% variant probes for DNA methylation, and obtained 9 factors, two of which explained a good portion of the RNA-seq data's variance.\r\n\r\nHere's the plot.\r\n\r\n![image](https://user-images.githubusercontent.com/25909144/37911571-63ba1560-30de-11e8-8b63-d0e46c73544e.png)\r\n\r\n\r\n"}}
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