pancreas.integrated <- RunPCA(pancreas.integrated, verbose = FALSE) pancreas.integrated <- RunUMAP(pancreas.integrated, dims = 1:30) plots <- DimPlot(pancreas.integrated, group.by = c("tech", "celltype"), combine = FALSE) plots <- lapply(X = plots, FUN = function(x) x + theme(lege...
该方法返回降维(即integrated.cca),可用于可视化和无监督聚类分析。为了评估性能,我们可以使用 seurat_annotations 元数据列中预加载的单元格类型标签。 ifnb <- IntegrateLayers(object = ifnb, method = CCAIntegration, orig.reduction = "pca", new.reduction = "integrated.cca", verbose = FALSE) # re-...
结果存放在@assays$integrated中,此时仅有@assays$integrated@data 使用的assay修改为integrated 需要使用ScaleData()函数对@assays$integrated@data进行标准化,生成@assays$integrated@scale.data,随后的降维和聚类分析也是基于@assays$integrated@scale.data进行 4. 整体过程 library(Seurat)library(dplyr)require(ggplot2)r...
Cloud Studio代码运行 DefaultAssay(pbmc_seurat)<-"integrated"pbmc_seurat<-ScaleData(pbmc_seurat,verbose=F)pbmc_seurat<-RunPCA(pbmc_seurat,npcs=30,verbose=F)pbmc_seurat<-RunUMAP(pbmc_seurat,reduction="pca",dims=1:30,verbose=F)### 可视化 p1<-DimPlot(...
原数据的@assays$RNA@counts和@assays$RNA@data都不会被改变,而是放在整合后数据的RNA这个slot中 原数据的@assays$RNA@scale.data被清除 整合后,去除完批次效应的数据存放在@assays$integrated@data中,对此数据进行ScaleData()之前,@assays$integrated@scale.data是没有数据的 ...
immune.anchors<-FindIntegrationAnchors(object.list=ifnb.list,anchor.features=features)#thiscommand creates an'integrated'data assay immune.combined<-IntegrateData(anchorset=immune.anchors) 执行整合分析 现在,我们可以对所有细胞进行单次整合分析! 代码语言:javascript ...
Seurat v5 整合过程旨在返回一个单维缩减,捕获多个层之间共享的方差源,以便处于相似生物状态的细胞能够聚集。该方法返回降维(即integrated.cca),可用于可视化和无监督聚类分析。为了评估性能,我们可以使用 seurat_annotations 元数据列中预加载的单元格类型标签。
integrated.data <- Integration(object.list = integration.list) ``` 3.可视化集成结果:使用`DimPlot`函数将集成结果可视化。例如,使用以下代码可视化集成结果的tSNE图: ```R DimPlot(object = integrated.data, reduction = "integrated", group.by = "dataset") ``` 以上就是Seurat官方教程第三部分的内容。
DefaultAssay(ifnb.combined) <- "integrated" ifnb.combined <- ScaleData(ifnb.combined, verbose = FALSE) ifnb.combined <- RunPCA(ifnb.combined, npcs = 30, verbose = FALSE) ifnb.combined <- RunUMAP(ifnb.combined, reduction = "pca", dims = 1:30) ifnb.combined <- FindNeighbors(ifnb...
DefaultAssay(pancreas.integrated) <- "integrated" pancreas.integrated <- ScaleData(pancreas.integrated, verbose = FALSE) pancreas.integrated <- RunPCA(pancreas.integrated, npcs = 30, verbose = FALSE) pancreas.integrated <- RunUMAP(pancreas.integrated, reduction = "pca", dims = 1:30) ...