默认用法(针对两个单细胞亚群进行差异分析)如下所示: markers_2 <- FindMarkers(sce.sub.Endo, ident.1="HE", ident.2="CI") head(x = markers_2) # 就143个基因 > head(markers_2) p_val avg_log2FC pct.1 pct.2 p_val_adj CXCL8 1.920167e-57 0.4110767 0.239 0.485 3.840335e-54 ITPKC 2....
在查看输出时,我们建议寻找pct.1和pct.2之间表达差异较大且倍数变化较大的标记。例如,如果pct.1= 0.90 和pct.2= 0.80,它可能不是正确的标记。但是,如果pct.2= 0.1 而不是,更大的差异会更有说服力。此外,有趣的是,如果大多数表达标记的细胞都在我感兴趣的簇中,如pct.1很低,比如 0.3,它可能不是正确的...
AI代码解释 markers_df<-FindMarkers(object=sce,ident.1=0,min.pct=0.25)print(x=head(markers_df))markers_genes=rownames(head(x=markers_df,n=5))VlnPlot(object=sce,features=markers_genes,log=T)FeaturePlot(object=sce,features=markers_genes)markers_df<-FindMarkers(object=sce,ident.1=1,min.pct=...
AI代码解释 markers_2<-FindMarkers(sce.sub.Endo,ident.1="HE",ident.2="CI")head(x=markers_2)# 就143个基因>head(markers_2)p_val avg_log2FC pct.1pct.2p_val_adjCXCL81.920167e-570.41107670.2390.4853.840335e-54ITPKC2.792237e-430.25226400.2480.4145.584474e-40THY15.018897e-42-0.30357010.9910.55...
1.FindAllMarkers FindAllMarkers(object, ident.1, ident.2 = NULL, genes.use =NULL, thresh.use= 0.25, test.use ="bimod", min.pct = 0.1, min.diff.pct= 0.05,print.bar = TRUE, only.pos =FALSE, max.cells.per.ident= Inf,return.thresh = 0.01, do.print=FALSE, ...
Is it because the function only takes a subset of cells to calculate pct.1 and pct.2 but takes all cells to calculate avg_logFC? So that the gene could be expressed in some cells but not in the ones that have been taken to calculate pct.1 and pct.2? I would be very grateful if...
cells.2 = NULL, features = NULL, reduction = NULL, logfc.threshold = 0.25, test.use = "wilcox", min.pct = 0.1, min.diff.pct = -Inf, verbose = TRUE, only.pos = FALSE, max.cells.per.ident = Inf, random.seed = 1, latent.vars = NULL, ...
(object=pbmc3k,ident.1='CD8 T',ident.2='NK',group.by='seurat_annotations',assay='RNA',slot='data',features='CD8A')|+++++++++++++++++++++++++|100%elapsed=00sp_valavg_log2FCpct.1pct.2p_val_adjCD8A9.280891e-181.7550370.5060.0841.272781e-13>>#the process I guess how seurat...
cells.1 =NULL, cells.2 =NULL, features =NULL, reduction =NULL, logfc.threshold =0.25, test.use ="wilcox", min.pct =0.1, min.diff.pct = -Inf, verbose =TRUE, only.pos =FALSE, max.cells.per.ident =Inf, random.seed =1,
pct.1在当前cluster细胞中检测到该基因表达的细胞⽐例 pct.2在其它cluster细胞中检测到该基因表达的细胞⽐例 avg_logFC两组间平均logFC,Seuratv4默认log2。正值表⽰特征在第⼀组中表达得更⾼ p_val未调整P-value,数值越⼩越显著 p_val_adj基于使⽤数据集中所有特征的bonferroni校正,校正后的p值 ...