您可以通过增加参数k.anchor来增加对齐的强度,该参数默认设置为 5。将这一参数增加到20将有助于对齐这些亚群。 immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, anchor.features = features, reduction ="rpca", k.anchor = 20) immune.combined <- IntegrateData(anchorset = immune.anchors...
reduction="rpca",k.anchor=20)immune.combined<-IntegrateData(anchorset=immune.anchors)immune.combined<-ScaleData(immune.combined,verbose=FALSE)immune.combined<-RunPCA(immune.combined,npcs=30,verbose=FALSE)immune.combined<-RunUMAP(immune.combined,reduction="pca",dims=1:30)immune.combined<-FindNeighbors(...
sceList.integrated <- IntegrateData(anchorset = anchors, dims = 1:50) 按照RPCA流程,将参考数据改为1PD 1PD 1SD,运行成功 anchors <- FindIntegrationAnchors(object.list = sceList, reference = c(1,4,23), reduction = "rpca", dims = 1:50) sceList.integrated <- IntegrateData(anchorset = an...
您可以通过增加参数k.anchor来增加对齐的强度,该参数默认设置为 5。将这一参数增加到20将有助于对齐这些亚群。 代码语言:javascript 复制 immune.anchors<-FindIntegrationAnchors(object.list=ifnb.list,anchor.features=features,reduction="rpca",k.anchor=20)immune.combined<-IntegrateData(anchorset=immune.anchors)...
anchors <- FindIntegrationAnchors(object.list = bm280k.list, reference = c(1, 2), reduction ="rpca", dims = 1:50) bm280k.integrated <- IntegrateData(anchorset = anchors, dims = 1:50) bm280k.integrated <- ScaleData(bm280k.integrated, verbose = FALSE) ...
然后,我们使用 FindIntegrationAnchors() 函数识别 anchors,该函数将 Seurat 对象列表作为输入,并使用这些 anchors 和 IntegrateData() 函数将两个数据集整合在一起。 immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, anchor.features = features, reduction = "rpca") # 该命令创建一个 'integr...
anchors<-FindIntegrationAnchors(object.list=bm280k.list,reference=c(1,2),reduction="rpca",dims=1:50)bm280k.integrated<-IntegrateData(anchorset=anchors,dims=1:50)bm280k.integrated<-ScaleData(bm280k.integrated,verbose=FALSE)bm280k.integrated<-RunPCA(bm280k.integrated,verbose=FALSE)bm280k.integr...
anchors <- FindIntegrationAnchors(object.list = bm280k.list, reference = c(1, 2), reduction = "rpca", dims = 1:50) bm280k.integrated <- IntegrateData(anchorset = anchors, dims = 1:50) bm280k.integrated <- ScaleData(bm280k.integrated, verbose = FALSE) ...
immune.anchors<-FindIntegrationAnchors(object.list=ifnb.list,normalization.method="SCT",anchor.features=features)immune.combined.sct<-IntegrateData(anchorset=immune.anchors,normalization.method="SCT") Perform an integrated analysis 现在可以对所有细胞进行一次整合分析。
Error in idx[i, ] <- res[[i]][[1]] : number of items to replace is not a multiple of replacement length in IntegrateData()#6359 Closed @levinhein, I have also seen that when samples have large differences in cell numbers (10 fold) then also this problem comes. I was wondering if...