Create stress_check_all_classes.R

stress_check_all_classes.R

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+library(Seurat)
+library(SeuratDisk)
+lydia <- LoadH5Seurat('F://lydia.h5Seurat0.h5seurat')
+head(lydia)
+
+
+library(ggplot2)
+library(ggpubr)
+library(escape)
+library(patchwork)
+
+gene.sets1 <- getGeneSets(library = "C5", gene.sets = c('GOBP_NEURON_DEATH','GOBP_RESPONSE_TO_AXON_INJURY', 
+                                                        'GOBP_INFLAMMATORY_RESPONSE', 'GOBP_INFLAMMASOME_COMPLEX_ASSEMBLY', 
+                                                        'GOBP_ACUTE_INFLAMMATORY_RESPONSE', 'GOBP_CYTOKINE_PRODUCTION_INVOLVED_IN_INFLAMMATORY_RESPONSE',
+                                                        'GOBP_NEUROINFLAMMATORY_RESPONSE'),
+                          species = 'Mus musculus')
+
+ES <- enrichIt(obj = lydia, 
+               gene.sets = gene.sets1, 
+               groups = 1000)
+
+lydia <- AddMetaData(lydia, ES)
+
+ES2 <- data.frame(lydia[[]], Idents(lydia))
+colnames(ES2)[ncol(ES2)] <- "cluster"
+
+lyd_RGC <- ES2
+fun_range <- function(x) {                              # Create user-defined function
+  (x - min(x)) / (max(x) - min(x))}
+
+neude <- aggregate(lyd_RGC$GOBP_NEURON_DEATH, list(lyd_RGC$anno2, lyd_RGC$background), FUN = mean)
+neude$neude <- fun_range(x = neude$x) 
+resax <- aggregate(lyd_RGC$GOBP_RESPONSE_TO_AXON_INJURY, list(lyd_RGC$anno2, lyd_RGC$background), FUN = mean)
+resax$resax <- fun_range(x = resax$x) 
+
+acinf <- aggregate(lyd_RGC$GOBP_ACUTE_INFLAMMATORY_RESPONSE, list(lyd_RGC$anno2, lyd_RGC$background), FUN = mean)
+acinf$acinf <- fun_range(x = acinf$x) 
+cytpr <- aggregate(lyd_RGC$GOBP_CYTOKINE_PRODUCTION_INVOLVED_IN_INFLAMMATORY_RESPONSE, list(lyd_RGC$anno2, lyd_RGC$background), FUN = mean)
+cytpr$cytpr <- fun_range(x = cytpr$x) 
+infco <- aggregate(lyd_RGC$GOBP_INFLAMMASOME_COMPLEX_ASSEMBLY, list(lyd_RGC$anno2, lyd_RGC$background), FUN = mean)
+infco$infco <- fun_range(x = infco$x) 
+infre <- aggregate(lyd_RGC$GOBP_INFLAMMATORY_RESPONSE, list(lyd_RGC$anno2, lyd_RGC$background), FUN = mean)
+infre$infre <- fun_range(x = infre$x) 
+neure <- aggregate(lyd_RGC$GOBP_NEUROINFLAMMATORY_RESPONSE, list(lyd_RGC$anno2, lyd_RGC$background), FUN = mean)
+neure$neure <- fun_range(x = neure$x) 
+
+final <- neude
+final$neude <- neude$neude
+final$resax <- resax$resax
+final$acinf <- acinf$acinf
+final$cytpr <- cytpr$cytpr
+final$infco <- infco$infco
+final$infre <- infre$infre
+final$neure <- neure$neure
+
+head(final)
+final$injury <- final$resax + final$neude
+final$inflam <- final$acinf + final$cytpr + final$infco + final$infre + final$neure
+
+final$injury_new <- fun_range(x = final$injury) 
+final$inflam_new <- fun_range(x = final$inflam) 
+
+testnew <- final %>% select(Group.1, injury_new, inflam_new, Group.2) %>%
+  pivot_longer(., cols = c(injury_new, inflam_new), names_to = "Var", values_to = "Val")
+
+head(testnew)
+ggplot(testnew, aes(x = reorder(Group.1, +Val), y = Val, fill = Group.2, color = Group.2, group = Group.2)) +
+  geom_point() + geom_line() + theme_bw() + coord_flip() + facet_wrap(~Var)