Spatial proteogenomics reveals distinct and
evolutionarily conserved hepatic macrophage
niches,Cell,2022 Single-Cell, Single-Nucleus, and
Spatial RNA Sequencing of the Human
Liver Identifies Cholangiocyte and
Mesenchymal Heterogeneity,Hepatology communications,2021 数据读取-空转 library(Seurat)"rawdata_st/" )"rawdata_st/" , assays)'D1' ,'D2' ,'H1' ,'D3' ,'H2' )for (i in 1 :length(dir)){"[aA-zZ]+" )"percent.mt" ]] <- PercentageFeatureSet(spatial, pattern = "^MT[-]" )"percent.mt" ) + ggtitle(unique(x$orig.ident)) +"none" , axis.text.x = element_blank())"percent.mt" ) + theme(legend.position = "right" )2 )1 ]], spatial.list[2 :length(spatial.list)])"Spatial" , verbose = T)"SCT" , verbose = T)30 , reduction="pca" ) "orig.ident" ,assay.use = "SCT" )"harmony" , dims = 1 :20 )"harmony" , dims = 1 :20 )0.5 )"umap" , label = TRUE,group.by = 'SCT_snn_res.0.5' ,pt.size = 1 )数据读取-单细胞 #读取数据 单细胞部分"./rawdata_sc/" )'.h5' )'Ob1' ,'Ob2' ,'Ob3' ,'Ob4' ,'Ob5' ,'Ob6' ,'Le1' ,"Le2" )for (i in 1 :length(fs)){'_' ,simplify = T)[,1 ]3 , min.features = 200 )"[aA-zZ]+" )1 ]], scRNA.list[2 :length(scRNA.list)])"percent.mt" ]] <- PercentageFeatureSet(scRNA, pattern = "^MT-" )"percent.rb" ]] <- PercentageFeatureSet(scRNA, pattern = "^RP[LS]" )8000 &nFeature_RNA>300 &percent.mt<25 )"LogNormalize" , scale.factor = 10000 )"vst" , nfeatures = 3000 )"orig.ident" , max.iter.harmony = 20 )"harmony" , dims = 1 :30 , verbose = FALSE)"harmony" ,dims = 1 :30 , verbose = FALSE)for (res in c (0.01 , 0.05 , 0.1 , 0.2 , 0.3 , 0.5 ,0.8 ,1 ) ) {"RNA_snn" , 1 )'#e41a1c' ,'#377eb8' ,'#4daf4a' ,'#984ea3' ,'#ff7f00' ,'#ffff33' ,'#a65628' ,'#f781bf' )@meta .data)[grep("RNA_snn_res" , colnames(scRNA@meta .data))]) @meta .data, prefix = "RNA_snn_res." )"RNA_snn_res.0.3" "RNA" 'all' ,assay='RNA' ,slot='data' ,mu=1 ,n_genes_user=100 )new .cluster.ids <- c("Hep1" ,"Hep2" , "Hep3" ,"Hep4" ,"LSEC" ,"Kup" ,"HSC" ,"NKT" ,"Cho" , "UN1" , "CV_EC" , "UN2" ,"Plasma" )new .cluster.ids) <- levels(scRNA)new .cluster.ids)@active .ident)@active .ident去卷积-RCTD # RCTD 'RNA' ]]@counts )'barcode' , 'cell_type' )'Ob' )'RNA' ]]@counts )'barcode' , 'cell_type' )'Le' )'UN1' )'RNA' ]]@counts )'barcode' , 'cell_type' )# 拆分空间样本一一去卷积 --对应整体单细胞数据库"orig.ident" )for (i in slice){@images [[i]]@coordinates [,c(4 ,5 )]"x" ,"y" )@assays $Spatial@counts , 10 )'doublet' )@results $results_dffor (i in slice){if (str_extract_all(i,"[aA-zZ]+" )=='H' ){@images [[i]]@coordinates [,c(4 ,5 )]"x" ,"y" )@assays $Spatial@counts , 10 )'doublet' )@results $results_dfelse @images [[i]]@coordinates [,c(4 ,5 )]"x" ,"y" )@assays $Spatial@counts , 10 )'doublet' )@results $results_df区分正常和疾病组的数据库进行去卷积效果会优于整个数据库 基因集评分 ### 读取基因列表'../fatty.txt' ,header = T,check.names = F,sep = '\t' )'GTs' "geneset" ,geneset$GTs)"geneset.gmt" ,quote=F,sep="\t" ,row.names=T,col.names=F)scMetabolsimlibrary (scMetabolism) library (rsvd) Seurat (obj = scRNA, method = "VISION" , imputation = F, ncores = 1 , metabolism.type = "KEGG" ) metabolism (obj = scRNA, pathway = "geneset" , dimention.reduction.type = "umap" , dimention.reduction.run = F, size = 1 ) frame (scRNA@assays$METABOLISM$score) =as.data.frame(t(vision))AUCelllibrary (AUCell) AUCell_buildRankings (scRNA@assays$RNA@data, nCores=1 , plotStats=TRUE) AUCell_calcAUC (geneset, cells_rankings,nCores =1 , aucMaxRank=nrow(cells_rankings) *0.1)numeric (getAUC(cells_AUC) ['GTs', ])library (irGSEA) score (object = scRNA, assay = "RNA" ,"data" , seeds = 123 , ncores = 1 ,msigdb = F,'UCell' ,'singscore' ,'ssgsea' ) , = 'Gaussian' )@assays $UCell@counts )@assays $singscore@counts )@assays $ssgsea@counts )'Add' )0 -1 标准化return ((x-min(x))/(max(x)-min(x)))}2 , normalize)@meta .data=cbind(scRNA@meta .data,score)45 , face="italic" , hjust=1 ), axis.text.y = element_text(face="bold" )) + theme(legend.position="right" ) + labs(title = "cluster markers" , y = "" , x="" )多种基因集评分手段比较 # 关注细胞类型"Hep3" )2.5 ,'FAT_highHEP' ,'FAT_lowHEP' )"FAT_group" 'RNA' ]]@counts )'barcode' , 'cell_type' )for (i in slice){@images [[i]]@coordinates [,c(4 ,5 )]"x" ,"y" )@assays $Spatial@counts , 10 )'doublet' )@results $results_df# 可能是因为样本来源不同的原因，单细胞的发现无法在空间中定位 stRNA=AddModuleScore(stRNA,features = geneset,name = 'Add' ) SpatialFeaturePlot(stRNA, features = "Add1" , pt.size.factor = 1 ,0.1 ,1 ), min.cutoff = 0 , max.cutoff = 1 )
