res<-FindAllMarkers(object=scRNA,only.pos=TRUE,min.pct=0.25,logfc.threshold=0.25)end_time<-Sys.time()# 记录终止时间 end_time-start_time # 计算时间差 # Time differenceof3.996232secs # top5library(dplyr)top5<-res%>%group_by(cluster)%>%# 按照cluster分析arrange(desc(avg_log2FC),.by_gro...
arrange(desc(avg_log2FC), .by_group=T) %>% top_n(5, avg_log2FC) # 对每个分组选择avg_log2FC排名前5的基因 top5 运行时间是3.996232秒 logfc.threshold: log2倍数变化(log fold change, LogFC)的阈值。默认是0.1, min.pct: 基因在至少一个群体中的表达比例阈值。默认是0.01,即基因需要在至少1%...
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, random.seed= 1) 参数...
AI代码解释 library(future)# check the current active planplan()plan("multiprocess",workers=4)plan()start=Sys.time()sce.markers<-FindAllMarkers(object=sce,only.pos=TRUE,min.pct=0.25,thresh.use=0.25)end=Sys.time()dur=end-start dur 可以看到是 Time difference of 1.608828 mins ,可以看到针对全...
(all.markers) p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene GZMB 1.701079e-192 4.628285 0.961 0.084 2.332860e-188 14 GZMB FGFBP2 1.811563e-161 3.777668 0.877 0.082 2.484377e-157 14 FGFBP2 IGFBP7 1.204238e-158 3.149206 0.548 0.009 1.651492e-154 14 IGFBP7 GNLY 2.254041e-153 ...
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,random....
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 ...
library('Seurat',lib.loc="~/Documents/myRpackages/") packageVersion('Seurat') [1] ‘4.0.3’#or#library('Seurat')#packageVersion('Seurat')#[1] ‘4.3.0’data<-readRDS('dataset.rds') Idents(data)<-'seurat_clusters'markers<-FindAllMarkers(data,assay='RNA',logfc.threshold=0.25,min.pct=...
> # Finding differentially expressed features (cluster biomarkers) > b6mnn.markers <- FindAllMarkers(object = b6mnn, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25, slot = "data") Calculating cluster 0 Calculating cluster 1 Calculating cluster 2 Calculating cluster 3 Calculating clu...
I have a doubt about the output of FindAllMarkers, I know that pct.1 is the percentage of features "A" in cluster 1 but I am not sure about pct.2, Is it the percentage of that features "A" in all the other clusters ? since I am comparing one vs the rest? object.markers <- ...