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11_MANA_score.R
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11_MANA_score.R
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# MANA score calculation and plotting:
library(Seurat)
library(ggplot2)
library(dplyr)
library(ggpubr)
library(corrplot)
library(RColorBrewer)
library(ggthemes)
library(ggbeeswarm)
library(scales)
library(forcats)
CXCL13<-list(c("CXCL13","HLA-DRA","HLA-DRB5","HLA-DQA1","HLA-DRB1","HLA-DQB1","CCL3","GZMA","GEM","ENTPD1","HLA-DPA1","TNS3","MIR4435-2HG","IFNG","HLA-DPB1","CD8A","CD8B"))
good_samples <- c("T1","T2","T4","T5","T6","T7","T8","T9","T10","T11","T12","T13","T14","T15","T16","T17")
cd8t_clusters <- c("CD8_C1","CD8_C2","CD8_C3","CD8_C4","CD8_C5","CD8_C6","CD8_C7")
setwd("/mnt/sdc/singlecell/data/")
file1 <- readRDS("T_total_3_26.rds")
file1 <- subset(file1, subset = orig.ident %in% good_samples)
file1 <- AddModuleScore(object=file1, features=CXCL13, name="CXCL13_score")
file1 <- AddModuleScore(object=file1, features=flu, name="flu_score")
#colnames([email protected])[20] <- "CXCL13_score"
#saveRDS(file1,"T_total_12_10_MANAscore.rds")
file2 <- subset(file1, subset = Cluster %in% cd8t_clusters)
file1[["group"]] <- ifelse(file1$orig.ident %in% c("T1","T5","T6","T8","T10","T12","T13","T14","T16","T17"),"LUAD","AISMIA")
file2[["group"]] <- ifelse(file2$orig.ident %in% c("T1","T5","T6","T8","T10","T12","T13","T14","T16","T17"),"LUAD","AISMIA")
head([email protected])
ggplot([email protected], aes(x=group, y=CXCL13_score1, fill=group)) +
geom_boxplot() +
labs(x="Group", y = "CXCL13_score1", fill="Group") +
#scale_fill_brewer(palette="Blues") +
theme_classic()+
theme(axis.text.x = element_text(face = "bold", size=13),
axis.text.y = element_text(size=13),
axis.title=element_text(size=14),
panel.background = element_rect(fill = 'white'),
axis.line.x = element_line(color="black"),
axis.line.y = element_line(color="black"),
legend.title = element_text(size=13),
legend.text = element_text(size=12))+
stat_compare_means()+
scale_fill_brewer(palette="Set1")
ggsave("/mnt/sdc/singlecell/analysis/CXCL13_score1_MANAscore.pdf", plot=last_plot(), width=6, height=8, dpi=600)