{"action":"closed","issue":{"url":"https://api.github.com/repos/satijalab/seurat/issues/1501","repository_url":"https://api.github.com/repos/satijalab/seurat","labels_url":"https://api.github.com/repos/satijalab/seurat/issues/1501/labels{/name}","comments_url":"https://api.github.com/repos/satijalab/seurat/issues/1501/comments","events_url":"https://api.github.com/repos/satijalab/seurat/issues/1501/events","html_url":"https://github.com/satijalab/seurat/issues/1501","id":440340474,"node_id":"MDU6SXNzdWU0NDAzNDA0NzQ=","number":1501,"title":"find marker gene when using SCTranform normalization method","user":{"login":"wupeng2","id":48047291,"node_id":"MDQ6VXNlcjQ4MDQ3Mjkx","avatar_url":"https://avatars2.githubusercontent.com/u/48047291?v=4","gravatar_id":"","url":"https://api.github.com/users/wupeng2","html_url":"https://github.com/wupeng2","followers_url":"https://api.github.com/users/wupeng2/followers","following_url":"https://api.github.com/users/wupeng2/following{/other_user}","gists_url":"https://api.github.com/users/wupeng2/gists{/gist_id}","starred_url":"https://api.github.com/users/wupeng2/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/wupeng2/subscriptions","organizations_url":"https://api.github.com/users/wupeng2/orgs","repos_url":"https://api.github.com/users/wupeng2/repos","events_url":"https://api.github.com/users/wupeng2/events{/privacy}","received_events_url":"https://api.github.com/users/wupeng2/received_events","type":"User","site_admin":false},"labels":[],"state":"closed","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":1,"created_at":"2019-05-04T16:18:50Z","updated_at":"2019-05-10T15:42:51Z","closed_at":"2019-05-10T15:42:51Z","author_association":"NONE","body":"hello,\r\nthans for your excellent jobs. \r\nbut i still fell confused about find marker gene for cluster for single sample after read your vignette.\r\nthere are two way to find marker.\r\noption 1:cluster and find marker use SCTransform data \r\nfresh.second <- SCTransform(object = fresh.second, vars.to.regress = \"percent.mt\", verbose = FALSE,return.only.var.genes = FALSE)\r\nfresh.second <- RunPCA(object = fresh.second, features = VariableFeatures(object = fresh.second))\r\npca<-1:30\r\nfresh.second <- RunUMAP(fresh.second, dims = pca)\r\nfresh.second <- RunTSNE(fresh.second, dims = pca)\r\n\r\nfresh.second <- FindNeighbors(fresh.second, dims = pca)\r\nfresh.second <- FindClusters(fresh.second, resolution = 0.5)\r\nfresh.second.markers <- FindAllMarkers(fresh.second, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)\r\n\r\noption2:cluster by SCTransform data  and find marker use “RNA” data \r\nfresh.second <- SCTransform(object = fresh.second, vars.to.regress = \"percent.mt\", verbose = FALSE,return.only.var.genes = FALSE)\r\nfresh.second <- RunPCA(object = fresh.second, features = VariableFeatures(object = fresh.second))\r\npca<-1:30\r\nfresh.second <- RunUMAP(fresh.second, dims = pca)\r\nfresh.second <- RunTSNE(fresh.second, dims = pca)\r\nfresh.second <- FindNeighbors(fresh.second, dims = pca)\r\nfresh.second <- FindClusters(fresh.second, resolution = 0.5)\r\nDefaultAssay(object = fresh.second) <- \"RNA\"\r\nfresh.second <- NormalizeData(object =fresh.second, normalization.method = \"LogNormalize\", scale.factor = 10000)\r\nall.genes <- rownames(x = pbmc)\r\nfresh.second  <- ScaleData(object = fresh.second, features = all.genes)\r\nfresh.second .markers <- FindAllMarkers(fresh.second , only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)\r\nwhich one do you recommend?\r\nthanks \r\nbest wishes!"}}
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