Seurat 对象中assays用于存储表达矩阵, counts存储原始数据(稀疏矩阵),data存储Normalize()之后的数据,scale.data存储ScaleData()缩放后的数据,SCT存储标准化之后的数据, meta.data存储细胞注释信息(或称为临床信息), active.assay存储默认的矩阵名, active.ident存储默认的细胞注释信息(或称为临床信息)。 library(Seura...
(counts = pbmc.data, project = "pbmc3k", min.cell = 3, min.features = 200) pbmc.obj # An object of class Seurat # 13714 features across 2700 samples within 1 assay # Active assay: RNA (13714 features, 0 variable features) # 1 layer present: counts # 细胞名称重命名 pbmc.obj <-...
"package")#[1]"Matrix"scRNA<-CreateSeuratObject(counts=counts)scRNA#An object of class Seurat#33694features across9112samples within1assay#Active assay: RNA (33694 features, 0 variable features
Active assay:RNA(13714features,0variable features)>head(pbmc$RNA@data[,1:5])6x5sparse Matrix ofclass"dgCMatrix"AAACATACAACCAC-1AAACATTGAGCTAC-1AAACATTGATCAGC-1AL627309.1...AP006222.2...RP11-206L10.2.
immune.combined <- IntegrateData(anchorset = immune.anchors) 1. 2. 3. 整合分析 我们对修正后的数据进行下游分析,原始数据仍然存在于“RNA”assay中 DefaultAssay(immune.combined) <- "integrated" #运行标准化可视化与聚类 immune.combined <- ScaleData(immune.combined, verbose = FALSE) ...
这里要求细胞中基因数目不能小于200,且基因至少在三个细胞中有表达,否则过滤掉pbmc <- CreateSeuratObject(counts = pbmc.data, project = "pbmc3k", min.cells = 3, min.features = 200)pbmc## An object of class Seurat ## 13714 features across 2700 samples within 1 assay ## Active assay: RNA ...
# Initialize the Seurat object with the raw (non-normalized data).pbmc <- CreateSeuratObject(counts = pbmc.data, project = 'pbmc3k', min.cells = 3, min.features = 200)pbmc## An object of class Seurat## 13714 features across 2700 samples within 1 assay## Active assay: RNA (13714 ...
并且自动使用PercentageFeatureSet()函数计算每个细胞中线粒体基因表达比例。 代码语言:javascript 复制 scRNA<-load_scfile(m)scRNA[[1]]An objectofclassSeurat33694features across300samples within1assay Active assay:RNA(33694features,0variable features)1layer present:counts[[2]]An objectofclassSeurat33694fe...
Active assay:RNA(17009features,2000variable features)2layers present:counts,data1other assay present:ADT1dimensional reduction calculated:spca 可以看到对象中有RNA 和 ADT 2个assay。确认下RNA和ADT的cell barcode 是否一致 二WNN 模态数据处理 1,RNA标准分析 ...
## Active assay: RNA (13714 features) Seurat对象就是一个S4类,里面装着单细胞数据集,如count矩阵、细胞矩阵、聚类信息都存储在这个容器中。我们可以通过查看这个对象来得到我们想要的信息。如我们想看一下前5个细胞的前5个基因的表达矩阵,可以这样使用: ...