--- title: "Combin fine clustering wth large dataset" author: "Luise A. Seeker" date: "25/10/2021" output: html_document --- ```{r} library(Seurat) library(dplyr) library(here) library(ggsci) ``` # Prepare colours ```{r} mypal <- pal_npg("nrc", alpha = 0.7)(10) mypal2 <-pal_tron("legacy", alpha = 0.7)(7) mypal3 <- pal_lancet("lanonc", alpha = 0.7)(9) mypal4 <- pal_simpsons(palette = c("springfield"), alpha = 0.7)(16) mypal5 <- pal_rickandmorty(palette = c("schwifty"), alpha = 0.7)(6) mypal6 <- pal_futurama(palette = c("planetexpress"), alpha = 0.7)(5) mypal7 <- pal_startrek(palette = c("uniform"), alpha = 0.7)(5) mycoloursP<- c(mypal, mypal2, mypal3, mypal4, mypal5, mypal6, mypal7, "black", "blue") ``` # Load datasets ```{r} astr <-readRDS(here("data", "single_nuc_data", "astrocytes", "HCA_astrocytes.RDS")) micro <- readRDS(here("data", "single_nuc_data", "microglia", "HCA_microglia.RDS")) vasc <- readRDS(here("data", "single_nuc_data", "vascular_cells", "HCA_vascular_cells.RDS")) neur <- readRDS(here("data", "single_nuc_data", "neurons", "HCA_neurons.RDS")) nad_ol <- readRDS(here("data", "single_nuc_data", "oligodendroglia", "srt_oligos_and_opcs_LS.RDS")) ``` # Extract relevant cluster information ```{r} names(astr@meta.data) astr_df <- data.frame(Barcode = astr$Barcode, Fine_cluster = astr$astrocytes_clu) names(micro@meta.data) micro_df <- data.frame(Barcode = micro$Barcode, Fine_cluster = micro$microglia_clu) names(vasc@meta.data) vasc_df <- data.frame(Barcode = vasc$Barcode, Fine_cluster = vasc$vascular_cells_clu) names(neur@meta.data) neur_df <- data.frame(Barcode = neur$Barcode, Fine_cluster = neur$neurons_clu) names(nad_ol@meta.data) nad_ol_df <- data.frame(Barcode = nad_ol$Barcode, Fine_cluster = nad_ol$ol_clusters_named) ``` # combine all generated dataframes ```{r} main_df <- rbind(astr_df, micro_df, vasc_df, neur_df, nad_ol_df) ``` # Detatch cell type datasets ```{r} remove(astr) remove(micro) remove(vasc) remove(neur) remove(nad_ol) ``` # Read in complete dataset ```{r} seur <- readRDS(here("data", "single_nuc_data", "all_cell_types", "srt_anno_01.RDS")) ``` ```{r} seur_met <- seur@meta.data main_df$match <- as.factor(ifelse(main_df$Barcode %in% seur$Barcode, "match", "no_match")) seur_met$match <- as.factor(ifelse(seur_met$Barcode %in% main_df$Barcode, "match", "no_match")) seur@meta.data$match <- seur_met$match DimPlot(seur, group.by = "match") ``` ```{r} summary(main_df$Barcode == seur_met$Barcode) barcode_oder <- seur_met$Barcode main_DF <- main_df[match(barcode_oder, main_df$Barcode),] summary(main_df$Fine_cluster) main_DF$Fine_cluster_final <- ifelse(main_DF$Barcode %in% main_df$Barcode, paste(main_DF$Fine_cluster), "Immune") summary(main_DF$Barcode == seur_met$Barcode) id_vector <- main_DF$Fine_cluster_final seur$Fine_cluster <- id_vector DimPlot(seur, reduction = "umap", label = F,cols = mycoloursP, group.by = "Fine_cluster") ``` ```{r} saveRDS(seur, here("data", "single_nuc_data", "all_cell_types", "srt_fine_anno_01.RDS")) ```