expressed_pct=0.1,#设置低表达的阈值 n_genes_user=100#每个cluster定义Top-N个marker gene)head(marker_cosg$names,3)#012#1CD7S100A8MS4A1#2CCL5TYMPCD79A#3GNLYS100A9TCL1Ahead(marker_cosg$scores,3)#012#10.63919170.89540420.6922908#20.63912670.83120830.5832425#30.63281480.81200450.5757478 简单可视化一下 代...
suppressMessages(library(Seurat)) data('pbmc_small',package='Seurat') # Check cell groups: table(Idents(pbmc_small)) #> #> 0 1 2 #> 36 25 19 ### # Run COSG: marker_cosg <- cosg( pbmc_small, groups='all', assay='RNA', slot='data', mu=1, n_genes_user=100) ### # ...
再使用COSG来试试:为了验证COSG的效率,这里每个cluster统一返回900个marker(相比较于Seurat返回的数量最大值800多)。 library(COSG)library(dplyr)Seurat_markers%>%count(cluster)starttime<-Sys.time()COSG_markers<-cosg(pbmc3k,groups='all',assay='RNA',slot='data',mu=1,n_genes_user=900)endtime<-...
Marker genes or genomic regions identified by COSG are more indicative and with greater cell-type specificity. COSG is ultrafast for large-scale datasets, and is capable of identifying marker genes for one million cells in less than two minutes. ...
COSG(COSine similarity-based marker Gene identification)是由来自哈佛医学院和Broad研究所博后Ming Dai等人开发,旨在从余弦相似度的角度鉴定cluster的marker gene,于2021年12月被Briefings in Bioinformatics接收。目前已将此方法分别包装为R包与Python包,分别对应Seurat与Scanny分析流程。