'update'

Real_data_analysis/dorsolateral_prefrontal_cortex.R

@@ -66,7 +66,7 @@ getXList <- function(seuList, genelist){
     indexList[[i]] <- (nr+1):(nrow(XList[[i]] )+nr)
     nr <- nr + nrow(XList[[i]] )
     y <- seuList[[i]]$layer_guess_reordered
-    y[is.na(y)] <- "WM"
+    y[is.na(y)] <- "NA"
     yList[[i]] <- y
     posList[[i]] <- cbind(seuList[[i]]$row, seuList[[i]]$col)
   }

Real_data_results/Rcode/OutputFigData.R

@@ -0,0 +1,188 @@
+
+
+# Fig 2a ------------------------------------------------------------------
+
+
+r <- 10
+pos10 <- posList[[r]]
+
+for(i in 1:4){
+  message("i = ", i)
+  pos10 <- cbind(pos10,hZ_umap3List_allsample[[i]][indexList[[r]], ])
+}
+
+dim(pos10)
+head(pos10)
+
+
+for(j in 1:4){
+  cluster_tmp <- clusterMat_sub[indexList[[r]],j]
+  pos10 <- cbind(pos10,cluster_tmp)
+}
+colnas <- c("coord x", "coord y", rep(names(hZ_umap3List_allsample[1:4]), each=3), names(hZ_umap3List_allsample[1:4]))
+colnames(pos10) <- colnas
+getwd()
+write.csv(pos10, file='tmp.csv')
+
+##### 2b #####
+dat_tmp <- Reduce(cbind,c(tsne2List[1:4], tsne2List[9]))
+dat_tmp <- cbind(dat_tmp, meta_data[,c(1,3)])
+colnas <- c(rep(names(tsne2List[c(1:4, 9)]), each=2), "Sample", "Domain")
+colnames(dat_tmp) <- colnas
+write.csv(dat_tmp, file='tmp.csv', row.names = F)
+
+##### 2c #####
+ariMat_sub <- cbind(ariMat_sub, "PASTE"=ari_paste)
+row.names(ariMat_sub) <- paste0("Sample", 1:12)
+write.csv(ariMat_sub, file='tmp.csv')
+
+##### 2d #####
+write.csv(corMat, file='tmp.csv')
+
+##### 2e&f #####
+
+dat <- as.data.frame(seu[["Spatial"]]@cell.embeddings)
+genes <- t(seu[['RNA']]@scale.data[union(toupper(gene_eachdomain), toupper(gene_assoc_pseudotime)),])
+dat_tmp <- cbind(dat, genes)
+write.csv(dat_tmp, file='tmp.csv')
+
+
+##### 2g #####
+write.csv(df1, file='tmp.csv')
+
+##### 2h #####
+dat_tmp <- gostres2$result
+write.csv(as.matrix(dat_tmp), file='tmp.csv')
+
+##### 3a #####
+
+ARI_tmp <- rbind(ariMat[,main_order_names2], ariVec[main_order_names2])
+row.names(ARI_tmp) <- c(paste0("Sample", 1:8), "Combined")
+write.csv(ARI_tmp,  file='tmp.csv')
+cLISI_tmp <- clisiMat[,main_order_names2]
+
+iLISI_tmp <- ilisiMat[,main_order_names2]
+
+write.csv(cbind(cLISI_tmp, iLISI_tmp),  file='tmp.csv')
+
+
+##### 3b #####
+dat_tmp <- Reduce(cbind,c(hZtsneList[c(1,3,4, 8)], hZtsneList[10]))
+dat_tmp <- cbind(dat_tmp, meta_data[,c(1,2)])
+colnas <- c(rep(names(hZtsneList[c(1,3,4, 8, 10)]), each=2), "Sample", "Domain")
+colnames(dat_tmp) <- colnas
+write.csv(dat_tmp, file='tmp.csv', row.names = F)
+
+##### 3c #####
+dat <- as.data.frame(t(seu[["RNA"]]@scale.data))
+dat$Domain <- Idents(seu)
+write.csv(dat, file='tmp.csv')
+
+
+##### 3d #####
+Mac_tmp <- round(MacR2Vec[main_order_names2], 2)
+write.csv(Mac_tmp, file='tmp.csv')
+
+
+##### 3e #####
+datList_here <- list()
+for(slice in 1:3){
+
+  for(jj in 1:6){
+    if(jj==1){
+      dats <- dat_List_all[[slice]][[jj]][1:3]
+    }else{
+      tmp <- dat_List_all[[slice]][[jj]][3]
+      dats <- cbind(dats, tmp)
+    }
+
+  }
+  datList_here[[slice]] <- dats
+}
+
+dat_all <- datList_here[[1]]
+dat_all$sample <- 1
+for(r in 2:3){
+  tmpdat <- datList_here[[r]]
+  tmpdat$sample <- r
+  dat_all <- rbind(dat_all, tmpdat)
+}
+write.csv(dat_all, file='tmp.csv')
+
+
+##### 3f #####
+dat_tmp <- dat1[,c(1:2,4)]
+write.csv(dat_tmp, file='tmp.csv', row.names = F)
+##### 3g #####
+
+dat_tmp <- as.data.frame(t(logcounts(sce.liver)[features_reorder,]))
+
+dat_tmp$Pseudotime  <- range01(sce.liver$Pseudotime)
+dat_tmp$Domain <- sce.liver$domain 
+
+write.csv(dat_tmp, file='tmp.csv')
+
+
+##### 4b #####
+head(datList[[1]])
+dat_tmp <- as.data.frame(datList[[1]])
+colnames(dat_tmp) <- c("Coord y", "Coord y", "Domain")
+dat_tmp$Sample <- 1
+for(r in 2:16){
+  message("r = ", r)
+  dat_tmp2 <- as.data.frame(datList[[r]])
+  colnames(dat_tmp2) <- c("Coord y", "Coord y", "Domain")
+  dat_tmp2$Sample <- r
+  dat_tmp <- rbind(dat_tmp, dat_tmp2)
+}
+write.csv(dat_tmp, file='tmp.csv')
+
+##### 4c #####
+
+dat_tmp <- Reduce(cbind,c(tsne2List[1:4], tsne2List[9]))
+dat_tmp <- cbind(dat_tmp, meta_data[,c(1,2)])
+colnas <- c(rep(names(tsne2List[c(1:4, 9)]), each=2), "Sample", "Domain")
+colnames(dat_tmp) <- colnas
+write.csv(dat_tmp, file='tmp.csv', row.names = F)
+
+##### 4d #####
+dat_tmp <- percentage_long
+write.csv(dat_tmp, file='tmp.csv', row.names = F)
+
+##### 4e #####
+dat_tmp <-  rbind(MacR2Vec[main_order_names2], ariMat[,main_order_names2])
+write.csv(dat_tmp, file='tmp.csv', row.names = F)
+
+##### 4f #####
+dat_tmp <- Reduce(rbind, datList[1:8])
+write.csv(dat_tmp, file='tmp.csv')
+
+
+
+##### 5b #####
+dat_tmp <- Reduce(rbind, datList)
+write.csv(dat_tmp, file='tmp.csv')
+
+##### 5c #####
+dat_tmp <- Reduce(cbind,c(hZtsneList[1:4], hZtsneList[9]))
+dat_tmp <- cbind(dat_tmp, meta_data[,c(1,2)])
+colnas <- c(rep(names(hZtsneList[c(1:4, 9)]), each=2), "Sample", "Domain")
+colnames(dat_tmp) <- colnas
+write.csv(dat_tmp, file='tmp.csv', row.names = F)
+
+
+#### 5d #########
+for(r in 1:4){
+  datList[[r]][,"Proportion2"] <- datList0[[r]]$Proportion
+  datList[[r]][,"Proportion3"] <- datList2[[r]]$Proportion
+}
+for(r in 1:4){
+  datList[[r]][,"Sample"] <- paste0("HCC", r)
+
+}
+dat_tmp <- Reduce(rbind, datList)
+write.csv(dat_tmp, file='tmp.csv')
+
+##### 5e #####
+dat_tmp <- Reduce(rbind, datList)
+write.csv(dat_tmp, file='tmp.csv')