在浪费一些时间之后,我发现了有团队推出了FeaturePlot_scCustom() function, 请移步 https://samuel-marsh.github.io/scCustomize/index.html 根据官网的介绍,代码如下:# Show specific features in each sample.p <- FeaturePlot_scCustom(seurat_object = object1 (1:length(p), function(i) p[[i]])所以最终的代码如下:# Show specific features in each sample.p <- FeaturePlot_scCustom
(2)scCustomize 修改 p11 <- FeaturePlot_scCustom(seurat_object = sce2, features = "CD3D") p22 <- FeaturePlot_scCustom ), pt.size = 1, split.by = "sample" ,ncol = 4) (2)scCustomize 中FeaturePlot_scCustom 函数 ,算是修正了这个小bug FeaturePlot_scCustom(seurat_object = sce2, features = "CD3D", split.by = "orig.ident
#### umap4:scCustomize library(viridis) library(Seurat) library(scCustomize) # 绘图 p <- FeaturePlot_scCustom list p_merge <- list() for(i in 1:length(g)) { # 打印动态 print(g[i]) # 绘图 p_merge[[i]] <- FeaturePlot_scCustom list p_merge <- list() for(i in 1:length(g)) { # 打印动态 print(g[i]) # 绘图 p_merge[[i]] <- FeaturePlot_scCustom
############## umap4:scCustomize library(viridis) library(Seurat) library(scCustomize) FeaturePlot_scCustom 当时还专门在群里问了来着哈哈哈哈) 还可以轻松地修改配色: # 修改颜色 # Set color palette pal <- viridis(n = 10, option = "D") FeaturePlot_scCustom
aggregated gene set scores using AddModuleScore() function from Seurat and then plotted using FeaturePlot scCustom
yarn add package-2@^1.0.0 yarn add package-3@beta { "dependencies": { "package-1": "1.2.3", "package
group.by="celltype",label = T);UMAP_celltypeIdents(sc_dataset) <- sc_dataset$celltypescCustomize::DimPlot_scCustom
monorepo项目通常会有这样的结构: myproject.git/ packages/ package-1/ package.json package
celltypeDimPlot(sce, reduction = "umap", label = TRUE, repel = T,pt.size = 0.5) scCustomize::DimPlot_scCustom
假设我们正在编译的是Package-1,这时候我们可以设置另外一个Package-2,用来告诉aapt,如果Package-2定义有和Package-1一样的资源,那么就用定义在Package-2的资源来替换掉定义在
a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6q7r8s9t0" package-2@^2.0.0: version "2.0.1" resolved "https://registry.npmjs.org/package
a1b2c3d4e5f6g7h8i9j0k1l2m3n4o5p6q7r8s9t0" package-2@^2.0.0: version "2.0.1" resolved "https://registry.npmjs.org/package