我们使用CellCycleScoring函数计算每个细胞的细胞周期得分,并将计算出的S期和G2/M期的评分保存在metadata中,以及细胞处于G2M,S或G1期的预测分类。通过设置set.ident = TRUE,则CellCycleScoring将Seurat对象中每个细胞的分组信息设置为其所处的细胞周期阶段。 # 使用CellCycleScoring函数计算细胞周期评分 marrow <- Cel...
我们使用CellCycleScoring函数计算每个细胞的细胞周期得分,并将计算出的S期和G2/M期的评分保存在metadata中,以及细胞处于G2M,S或G1期的预测分类。通过设置set.ident = TRUE,则CellCycleScoring将Seurat对象中每个细胞的分组信息设置为其所处的细胞周期阶段。 # 使用CellCycleScoring函数计算细胞周期评分marrow<-CellCycl...
Tcells <- CellCycleScoring(Tcells, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE) #细胞周期信息储存在CellCycle Tcells@meta.data$CellCycle <- Idents(Tcells) Idents(Tcells) <- "Lane" #idents函数可以直接支持赋值metadata内的变量信息,然后就可以调用该变量内的信息内容 ...
Hi Seurat team, I cannot reproduce the same cell cycle scoring in Seurat v5 as the previous versions. I have tried to assign an object using Seurat_4.3.0 which yields 50% of cells in G1 which is what we expected based on experimental data. However using the same object in Seurat v5 ...
sce.mergeTEN <- CellCycleScoring(sce.mergeTEN, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE) 报错如下: Error in cut_number(): ! Insufficient data values to produce 24 bins 解决办法: 在sc_object<- CreateSeuratObject(counts = sc_data, project = "GSM5222644", ...
接上节(跟着Cell学单细胞转录组分析(三):单细胞转录组数据质控(QC)及合并去除批次效应)。 数据合并之后,就需要跑标准的Seurat分析流程了。在《cell》文章中,作者还计算了细胞周期评分,因为我们收集到的细胞可能处于不同的分裂时期,所以看周期是很有必要的,尤其是针对具体的研究目的。在示例数据中,可以看到,各个样品...
#细胞周期s.genes <- cc.genes$s.genesg2m.genes <- cc.genes$g2m.genesTcells <- CellCycleScoring(Tcells, s.features = s.genes, g2m.features = g2m.genes,set.ident =TRUE)#细胞周期信息储存在CellCycleTcells@meta.data$CellCycle <- Idents(Tcells)...
Using Seurat28, these PCs were used to build a k-nearest neighbor graph of the cells, which was then partitioned into 21 transcriptionally distinct clusters. The analysis of cell cycle genes revealed that transcriptome heterogeneity was not dominated by cell cycle status (Supplementary Fig. S1h)....
3b). S and G2-M phase scores were calculated using the CellCycleScoring in Seurat with mouse cell-cycle phase genes (https://github.com/hbc/tinyatlas/blob/master/cell_cycle/Mus_musculus.csv). S and G2-M phase scores were statistically compared among proximal, distal and control ...
kid <- CellCycleScoring(kid, s.features = s.genes, g2m.features = g2m.genes, set.ident = TRUE) all.genes <- rownames(kid) kid <- ScaleData(kid, vars.to.regress = c("S.Score", "G2M.Score"), features = all.genes)