ssGSEA-山脊图-汇总18条神经通路合集

######--------ssGSEA---------##############
#加载18条神经通路合集
load("./score/ssGSEA/geneset_list_18.RData")
geneset_list
select_pathway_name <- c("G_protein_coupled_ACh_receptor_signalling",
                         "Response_to_ACh",
                         "ACh_receptor_binding")#个性化修改

# ssGSEA计算
expression_matrix <- as.matrix(MERGE@assays$RNA@data)# 提取表达矩阵

ssgsea_scores <- gsva(
  expr = expression_matrix, 
  gset.idx.list = geneset_list[select_pathway_name], 
  method = "ssgsea", 
  kcdf = "Gaussian", 
  abs.ranking = TRUE
)

ssgsea_scores[1:3,1:4]

# 将ssGSEA分数添加到 Seurat 对象
ssgsea_scores <- t(ssgsea_scores) # 转置，使每行对应细胞，每列对应基因集
MERGE <- AddMetaData(MERGE, metadata = as.data.frame(ssgsea_scores))
head(MERGE@meta.data)


# 画图
# 1.山脊图
score_columns <- c("G_protein_coupled_ACh_receptor_signalling","Response_to_ACh","ACh_receptor_binding")

for (score_name in score_columns) {
  p <- ggplot(MERGE@meta.data, aes_string(x = paste0(score_name), y = "cluster", fill = "cluster")) +
    geom_density_ridges(scale = 1.5, alpha = 0.7, color = "white") +
    scale_fill_manual(values = color_epi) +
    theme_bw() +
    theme(
      axis.text.x = element_text(size = 12, face = "plain"),
      axis.text.y = element_text(size = 12, face = "plain"),
      axis.title.x = element_blank(),  # 移除x轴标题
      axis.title.y = element_text(size = 14, face = "plain"),
      plot.title = element_text(size = 16, face = "bold", hjust = 0.5),  # 设置标题样式
      panel.grid.major = element_blank(),
      panel.grid.minor = element_blank(),
      legend.position = "none"
    ) +
    labs(
      title = paste0(score_name, " Score"),  # 主标题
      y = "", fill = NULL
    )
  
  # 保存图片
  ggsave(paste0("ssGSEA_", score_name, "_ridge.png"), p, height = 4, width = 7)
}