Seurat通过CreateSeuratObject函数创建对象后,将我们导入的UMI count原始稀疏矩阵储存在pbmc@assays[["RNA"]]@counts,此外Seurat自动计算每个细胞总的UMI count,即每一列数字之和,储存在pbmc@meta.data[["nCount_RNA"]];计算每个细胞总的基因数,即每一列中非0的行数,储存在pbmc@meta.data[["nFeature_RNA"]] ...
seurat_object@meta.data$group <- ifelse(seurat_object@meta.data$orig.ident == 'Cytokines', 'Cytokines','Untreated') table(seurat_object@meta.data$group) 可保存数据,或继续按需走Seurat后续流程。 2. h5格式 #相关R包载入: library(hdf5r) library(stringr) library(data.table) h5格式可直接使用Re...
作者安装好了centos8,并安装了R-4.3.2,通过install.paclages("Seurat")自动安装Seurat包。运行了很久很久后,安装完成,运行library(Seurat),发现报错: 载入需要的程辑包:SeuratObject Error: package or namespace load failed for ‘SeuratObject’ in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()...
>pbmc.data <- Read10X(data.dir="Rdata/filtered_gene_bc_matrices/hg19/") >pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k" , min.cells = 3,min.features = 200) >pbmc 提示信息告诉我们,生成的pbmc对象中有13714个基因,2700个细胞。 导入成功后,我们也可以在Rstudio中查看pbm...
An efficiently restructured Seurat object, with an emphasis on multi-modal data. We have carefully re-designed the structure of the Seurat object, with clearer documentation, and a flexible framework to easily switch between RNA, protein, cell hashing, batch-corrected / integrated, or imputed data...
Formalclass'Seurat'[package"SeuratObject"]with13slots..@ assays:Listof1...$RNA:Formalclass'Assay'[package"SeuratObject"]with8slots...@ counts:Formalclass'dgCMatrix'[package"Matrix"]with6slots...@ i:int[1:2238732]297380148163184186227229230...@ p:int[1:2639]077921313260422047415522630470947626...@...
(注意自己的电脑系统,Windows问题不大,Mac需要注意是arm64 还是x86_64. 选择对应的package安装) 代码语言:txt 复制 2. Matrix安装的问题: 上述安装包可以成功安装,但是在library()的时候就出现问题了: “..., unable to load shared object '/Users/jiajia/Library/R/x86_64/4.1/library/Matrix/libs/Matrix....
ERROR: lazy loading failedforpackage ‘SeuratData’ 我机智地放弃了,我现在电脑里面的SeuratObject_4和Seurat_4搭配的非常,并不想被破坏! [1] uwot_0.1.16 Matrix_1.6-1.1 biomaRt_2.56.1 [4] igraph_1.5.1 umap_0.2.10.0 tibble_3.2.1 [7] dplyr_1.1.3 RColorBrewer_1.1-3 pheatmap_1.0.12 ...
>library(dplyr)>library(Seurat)#导入pbmc数据>pbmc.data<-Read10X(data.dir="Rdata/filtered_gene_bc_matrices/hg19/")>pbmc<-CreateSeuratObject(counts=pbmc.data,project="pbmc3k",min.cells=3,min.features=200)>pbmc 提示信息告诉我们,生成的pbmc对象中有13714个基因,2700个细胞。
(status 2 uses the sf packageinplace of rgdal) Error: package or namespace load failedfor'SeuratObject’inloadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]): 载入了名字空间'Matrix’ 1.6-1,但需要的是>= 1.6.3 ...