1. rm(list = ls()) 
  2. load(file = "step1output.Rdata")
  3. load(file = "step2output.Rdata")
  4. #输入数据：exp和Group
  5. #Principal Component Analysis
  6. #http://www.sthda.com/english/articles/31-principal-component
  7. ##-methods-in-r-practical-guide/112-pca-principal-component-a
  8. ##nalysis-essentials
  9. # 1.PCA 图----
  10. dat=as.data.frame(t(exp))
  11. library(FactoMineR)
  12. library(factoextra) 
  13. dat.pca <- PCA(dat, graph = FALSE)
  14. pca_plot <- fviz_pca_ind(dat.pca,
  15.                          geom.ind = "point", # show points only (nbut not "text")
  16.                          col.ind = Group, # color by groups
  17.                          palette = c("#00AFBB", "#E7B800"),
  18.                          addEllipses = TRUE, # Concentration ellipses
  19.                          legend.title = "Groups"
  20. )
  21. pca_plot

2.top 1000 sd 热图——

  1. cg=names(tail(sort(apply(exp,1,sd)),1000))
  2. n=exp[cg,]
  1. # 直接画热图，对比不鲜明
  2. library(pheatmap)
  3. annotation_col=data.frame(group=Group)
  4. rownames(annotation_col)=colnames(n) 
  5. pheatmap(n,
  6.          show_colnames =F,
  7.          show_rownames = F,
  8.          annotation_col=annotation_col
  9. )
  1. pheatmap(n,
  2.          show_colnames =F,
  3.          show_rownames = F,
  4.          annotation_col=annotation_col,
  5.          scale = "row",   ##按行标准化，只关心一个基因在不同样本中的变化
  6.          breaks = seq(-3,3,length.out = 100)   ##设置颜色分辨范围
  7.          )

按行标准化