{"action":"created","issue":{"url":"https://api.github.com/repos/MarioniLab/scran/issues/11","repository_url":"https://api.github.com/repos/MarioniLab/scran","labels_url":"https://api.github.com/repos/MarioniLab/scran/issues/11/labels{/name}","comments_url":"https://api.github.com/repos/MarioniLab/scran/issues/11/comments","events_url":"https://api.github.com/repos/MarioniLab/scran/issues/11/events","html_url":"https://github.com/MarioniLab/scran/issues/11","id":333200917,"node_id":"MDU6SXNzdWUzMzMyMDA5MTc=","number":11,"title":"Default sigma change in mnnCorrect","user":{"login":"yueqiw","id":5046690,"node_id":"MDQ6VXNlcjUwNDY2OTA=","avatar_url":"https://avatars0.githubusercontent.com/u/5046690?v=4","gravatar_id":"","url":"https://api.github.com/users/yueqiw","html_url":"https://github.com/yueqiw","followers_url":"https://api.github.com/users/yueqiw/followers","following_url":"https://api.github.com/users/yueqiw/following{/other_user}","gists_url":"https://api.github.com/users/yueqiw/gists{/gist_id}","starred_url":"https://api.github.com/users/yueqiw/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/yueqiw/subscriptions","organizations_url":"https://api.github.com/users/yueqiw/orgs","repos_url":"https://api.github.com/users/yueqiw/repos","events_url":"https://api.github.com/users/yueqiw/events{/privacy}","received_events_url":"https://api.github.com/users/yueqiw/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":3,"created_at":"2018-06-18T09:46:58Z","updated_at":"2018-06-23T08:12:27Z","closed_at":"2018-06-23T08:12:27Z","author_association":"NONE","body":"Hi,\r\n\r\nThe default value of sigma has changed from 1.0 to 0.1, but the documentation still says: \r\n> The choice of sigma determines the extent of smoothing - a value of 1 is used by default to reflect the boundaries of the space after cosine normalization\r\n\r\nWhat's the reason behind the change? Is 0.1 a better parameter for most use cases? Thanks! "},"comment":{"url":"https://api.github.com/repos/MarioniLab/scran/issues/comments/399650736","html_url":"https://github.com/MarioniLab/scran/issues/11#issuecomment-399650736","issue_url":"https://api.github.com/repos/MarioniLab/scran/issues/11","id":399650736,"node_id":"MDEyOklzc3VlQ29tbWVudDM5OTY1MDczNg==","user":{"login":"yueqiw","id":5046690,"node_id":"MDQ6VXNlcjUwNDY2OTA=","avatar_url":"https://avatars0.githubusercontent.com/u/5046690?v=4","gravatar_id":"","url":"https://api.github.com/users/yueqiw","html_url":"https://github.com/yueqiw","followers_url":"https://api.github.com/users/yueqiw/followers","following_url":"https://api.github.com/users/yueqiw/following{/other_user}","gists_url":"https://api.github.com/users/yueqiw/gists{/gist_id}","starred_url":"https://api.github.com/users/yueqiw/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/yueqiw/subscriptions","organizations_url":"https://api.github.com/users/yueqiw/orgs","repos_url":"https://api.github.com/users/yueqiw/repos","events_url":"https://api.github.com/users/yueqiw/events{/privacy}","received_events_url":"https://api.github.com/users/yueqiw/received_events","type":"User","site_admin":false},"created_at":"2018-06-23T08:12:27Z","updated_at":"2018-06-23T08:12:27Z","author_association":"NONE","body":"Thanks for the detailed answer! \r\n\r\n> sigma=0.1 was used to generate most of the results in the paper\r\n\r\nI was under the impression that the paper used the previous default sigma of 1.0, since the main text of the paper did not discuss kernel bandwidth, and the Supplementary Note 5 says the following: \r\n> We choose the default Gaussian kernel width σ2 = 1 in the cosine normalized space\r\n\r\nBut of course, the code repository makes it clear that 0.1 was used in most cases. Thanks for posting the code!\r\n\r\nI only tried sigma values of 0.1, 0.3 and 1.0. I got very similar results (almost identical clustering and batch distribution) from 0.3 and 1.0, so I think I'll use either one of them. What I saw from sigma=0.1 was some cells from each batch establishing their own \"clusters\" away from other cells on the t-SNE plot,  and more batch separation than uncorrected data. I guess it may have something to do with the stability to the correction vectors that you mentioned. Anyway, I think I got good enough results. Thanks for the detailed explanation!\r\n\r\n"}}
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