A broad range of cell function assays and reagents are available to elucidate key cellular processes including apoptosis, cell proliferation, cell cycle and viability.
Cell Function In general, the functions of specific stem cells are characterized by designatedin vitrocell culture assays. Several functional assays have been used to evaluate stem andprogenitor cellfunction, including the colony-forming cell (CFC) assay, which is used to enumeratehematopoietic stem ...
function(X) strsplit(X, split = "CD4_")[[1]][2]) Tcells <- subset(Tcells.cut, subset = demux.doublet.call == "SNG") 这里可能会有小伙伴疑问:importDemux函数有什么作用? 这个函数其实就是把多个管道的样本信息导入到我们的S4对象中,然后可以通过解卷积的方法帮助我们识别双细胞状态的油包水样...
An object of class Seurat 36601 features across 3981 samples within 1 assay Active assay: RNA (36601 features, 0 variable features) 每个细胞检测到基因数量分布,绘制柱状图。(非必须) at_least_one_G <- apply(pbmc.data_G, 2, function(x) sum(x>0)) pdf("每个细胞检测到基因数量分布(G).pdf"...
("文献复现/GSE158492数据集/GSE158492_CD4.best.txt","文献复现/GSE158492数据集/GSE158492_CD4-8.best.txt","文献复现/GSE158492数据集/GSE158492_CD8.best.txt"),lane.names = c("CD4","CD4-8","CD8"))Tcells.cut[["Sample"]] <- sapply(meta("Sample"...
T cell function and fate can be influenced by several metabolites: in some cases, acting through enzymatic inhibition of α-ketoglutarate-dependent dioxygenases, in others, through post-translational modification of lysines in important targets. We show
signal of interest, potential batch effects for both cell type annotation and cell-to-cell network construction need to be removed. We corrected potential sample-specific and other batch effects using the Harmony algorithm with the ‘addHarmony’ function67. At the same time, the Harmony-fixed ...
function(file_path,slide_id){cell2loc<-read.table(file_path,sep=",",header=TRUE)row.names(cell2loc)<-cell2loc$spot_idcell2loc$spot_id<-NULLcell2loc$Lymphatic.Endothelial<-NULLassay<-subset(cell2loc,cell2loc$sample==slide_id)rownames(assay)<-sub(paste("^",slide_id,"_",sep="")...
folders=list.files('./',pattern='[123]$')folderslibrary(Seurat)scList = lapply(folders,function(folder){CreateSeuratObject(counts = Read10X(folder),project = folder,min.cells =3, min.features =200)}) BM <- merge(scList[[1]],y = c(scList[[2]],scList[[3]]),add.cell.ids = c...
library(GSEABase)all.sets<-lapply(names(markers.z),function(x){GeneSet(markers.z[[x]],setName=x)})all.sets<-GeneSetCollection(all.sets) 然后计算测序数据中每个细胞的基因表达排名 rankings<-AUCell_buildRankings(counts(sce.tasic),plotStats=FALSE,verbose=FALSE) ...