转录组——热图（pheatmap）

###########################
# 2020/11/18 heatmap
###########################
setwd(dir = "../muscle")
library(tidyverse)
# 1. 导入并筛选，差异基因
gene_info <- read.csv(file = "zd_gene_info.csv")
names(gene_info) <- c("gene_id","Swissprot_ID","Gene_symbol","Function")
data <- read.csv(file = "../muscle/res",
                 header = T,
                 sep = "\t" )
de_result <- left_join(data, gene_info, by = c("id" = "gene_id")) # 合并表格
all_gene_TMM <- read.csv(file = "gene_all.TMM.EXPR.matrix",sep = "\t")
names(all_gene_TMM) <- c("id","H1","H2","H3","L1","L2","L3")
plot_result <- left_join(de_result,all_gene_TMM, by = c("id"="id"))
deg <- select(plot_result , id , log2FoldChange, pvalue, padj,H1,H2,H3,L1,L2,L3) %>%  # 筛选相关列
  filter(abs(log2FoldChange)>1 & padj<0.05) %>%
  arrange(desc(abs(log2FoldChange)))
heat_map_reasult <- select(deg,-c(2:4)) %>% 
  column_to_rownames(var = "id")
top_de_exp <- slice(heat_map_reault,1:50)

# pheatmap 普通热图
# 需要用的表格，heat_map_reasult,top_de_exp
library(pheatmap)
pheatmap::pheatmap(log10(top_de_exp+1), # 标准化1
              #     cluster_cols = F, # 行列的聚类结果
              #     cluster_rows = F,
              #     show_colnames = F,
              #     annotation_col = sample_info[,c("?" "?")]
              #     annotation_col = select(sample_info,stage)
              #     cutree_rows = 3 # 根据聚类结果，将热图分开
              #     color =  colorRampPalette(c("green","white","red"))(100), # 分成100个颜色梯度
)
pheatmap::pheatmap(top_de_exp,
                   scale = "row") # 标准化2