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scripts/2_SigZones_Visualization.R

+library(Seurat)
+library(ggplot2)
+library(dplyr)
+library(openxlsx)
+
+########################
+### General Settings ###
+########################
+# Working dir
+wdir <- "~/Downloads/TIGET/Squadrito/scSquadrito/squadrito_livertumor2022_spatial"
+# Data dir
+data_dir <- paste(wdir, "data", sep = "/")
+# Results dir
+out_dir <- paste(wdir, "results", sep = "/")
+dir.create(path = out_dir, showWarnings = F)
+
+# Read Obj
+full_obj <- readRDS(file = paste(out_dir, "crc.integrated.rds", sep = "/"))
+DefaultAssay(full_obj) <- "Spatial"
+
+signatures <- list("KC_long" = c("Vsig4","Clec4f","Marco","Fcna","Cd5l", "C1qa","C1qb",
+                                 "C1qc","Slc40a1","Clec1b","Cd38","Ptgs1","Nr1h3"),
+                   "TAMs" = c("Lyz2", "Ahnak", "Itgam", "Chil3", "S100a6", "Anxa2",
+                              "Lyz1", "Mmp7", "Spp1", "Mmp12", "Timp1"),
+                   "IFNa_TAMs" = c("Ccl2","Chil3","Ly6c2","Arg1","Cxcl10","Slfn4","F10",
+                                   "Lyz2","Msrb1","Hp","Slfn1","Cebpb","Fcgr1","Ifi204",
+                                   "Plac8","Gm21188","Mafb","Tgm2","Ms4a4c","Ifi27l2a"),
+                   "Cancer_cells" = c("Phlda1","Krt18","Krt8","Lars2","Krt19","Epcam",
+                                      "Jun","Cldn7","Lgals4","Cldn4","Cldn3","Krt7",
+                                      "Tcim","Egr1","Sox4","2200002D01Rik","Axin2",
+                                      "Gpx2","Taf1d","Plk2"),
+                   "Hepatocytes" = c("Fabp1","Apoc1","Apoa2","Mt1","Alb","Serpina1e",
+                                     "Ttr","Gsta3","Serpina1c","Akr1c6","Gstm1",
+                                     "Serpina1a","Serpina1b","Gnmt","Apoc3","Cdo1",
+                                     "Bhmt","Rgn","Ass1","Ttc36")
+)
+
+for (sig in names(signatures)) {
+    full_obj <- AddModuleScore(object = full_obj, features = list(signatures[[sig]]), name = sig)
+}
+
+# IFN
+miDB_sig2 <- readRDS(paste(wdir, "reference", "miDB_sig3.rds", sep = "/")) #Ctrl
+ifn_sig <- miDB_sig2[["GOBP_RESPONSE_TO_TYPE_I_INTERFERON"]]
+ifn_sig_filt <- ifn_sig[ifn_sig %in% rownames(full_obj)]
+full_obj <- AddModuleScore(object = full_obj, features = list(ifn_sig_filt), name = "RespTypeIifn")
+
+full_df_sig <- data.frame()
+for (sig in c(names(signatures), "RespTypeIifn")) {
+    df <- full_obj@meta.data[, c(paste0(sig, "1"), "Zones", "TreatGroups", "orig.ident")]
+    colnames(df) <- c("Sig", "Zones", "RNA_Group", "Sample")
+    df$SigName <- sig
+    full_df_sig <- rbind(full_df_sig, df)
+}
+full_df_cor_group <- full_df_sig %>%
+    group_by(SigName, Zones, RNA_Group) %>%
+    summarise(MedianMS = median(Sig))
+min_sig <- full_df_cor_group %>% group_by(SigName) %>% summarise(SigMin = min(MedianMS))
+full_df_cor_group <- merge(full_df_cor_group, min_sig, by = "SigName")
+
+write.xlsx(x = list("MedianSig" = full_df_cor_group[,-5]),
+           file = paste(out_dir, "Full_SignaturesMedianModScore.xlsx", sep = "/"))
+
+p <- ggplot(full_df_cor_group, aes(x = Zones, y = MedianMS, color = RNA_Group,
+                                   fill = RNA_Group, group = RNA_Group,
+                                   ymin = SigMin, ymax = MedianMS)) +
+    theme_bw() +
+    theme(legend.position = "right",
+          legend.background = element_rect(color = "black"),
+          axis.text.x = element_text(angle = 45, hjust = 1)) +
+    #geom_line() +
+    geom_point() +
+    geom_ribbon(position = 'identity', alpha = .1,) +
+    xlab("") +
+    ylab("Median MScore") +
+    facet_wrap(.~SigName, ncol = 3, scales = "free")
+ggsave(filename = paste(out_dir, "Full_SignaturesMedianModScore.pdf", sep = "/"),
+       plot = p, width = 10, height = 7)