Seurat v5提供了一个新的技术,叫bridge integration,用于整合不同组学的实验结果,例如单独的scRNA-seq和scATAC-seq数据集。 这种方法利用了单独的多组学数据集作为分子“桥梁”,在低维空间中进行整合,并返回一个目标降维(例如integrated.rpca),旨在将不同批次中的共享细胞类型共嵌入。 整理一下目前提供的整合算法: CC...
combined <- FindNeighbors(combined, reduction = "integrated.rpca", dims = 1:30) combined <- FindClusters(combined, resolution = 1, cluster.name = "rpca_clusters") combined <- RunUMAP(combined, reduction = "integrated.rpca", dims = 1:30, reduction.name = "umap.rpca") p2 <- DimPlot(co...
immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, anchor.features = features, reduction ="rpca") # this command creates an 'integrated' data assay immune.combined <- IntegrateData(anchorset = immune.anchors) 现在,我们可以对所有细胞进行单次整合分析! # specify that we will perfor...
immune.anchors<-FindIntegrationAnchors(object.list=ifnb.list,anchor.features=features,reduction="rpca") #thiscommand creates an'integrated'dataassay immune.combined<-IntegrateData(anchorset=immune.anchors) 现在,我们可以对所有细胞进行单次整合分析! # specify that we will perform downstream analysis on the...
conserved cell type markers部分,用整合前的“RNA”或整合后的归一化”integrated“分析不会造成差异; 作为一般规则,我们总是建议对原始的“RNA“执行差异分析,而不是对批次校正等值执行差异分析,整合后的数据用于聚类。后续的分析建议建立在“RNA”上,因此在CCA之后,可以设置:DefaultAssay(sc_data) <- "RNA"。
immune.anchors<-FindIntegrationAnchors(object.list=ifnb.list,anchor.features=features,reduction="rpca") 代码语言:javascript 复制 #thiscommand creates an'integrated'data assay immune.combined<-IntegrateData(anchorset=immune.anchors) 现在,我们可以对所有细胞进行单次整合分析!
#CCAobj <- IntegrateLayers( object = obj, method = CCAIntegration, orig.reduction = "pca", new.reduction = "integrated.cca", verbose = FALSE)#RPCAobj <- IntegrateLayers( object = obj, method = RPCAIntegration, orig.reduction = "pca", new.reduction = "integrated.rpca", verbose = FALSE...
这里用CCA 和 RPCA 示例,其他的两种同样的方式,注意修改reduction.name。 代码语言:javascript 复制 ###CCA### obj<-FindNeighbors(obj,reduction="integrated.cca",dims=1:30)obj<-FindClusters(obj,resolution=2,cluster.name="cca_clusters")obj<-RunUMAP(obj,reduction="integrated.cca",dims=1:30,reduction...
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) ...
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...