相关性热图的完美解决方案 -- pheatmap包

相关性热图的完美解决方案 – pheatmap包

install.packages('pheatmap')# 安装包，加载数据
library(pheatmap)

# 生成测试数据集
test = matrix(rnorm(200),20,10)
# 取出1-10行，13579列，全部加3
test[1: 10, seq(1,10,2)] = test[1:10, seq(1, 10,2)]+3
# 取出11-20行，246810列，全部加1
test[11: 20, seq(2,10,2)] = test[11:20, seq(2, 10,2)]+1
# 取出15-20行，246810列，全部加5
test[15:20, seq(2,10,2)] = test[15:20, seq(2,10,2)]+5
> head(test)
         [,1]       [,2]     [,3]       [,4]     [,5]        [,6]
[1,] 5.862158  1.0025110 4.643383  1.3927443 3.078703 -0.70255575
[2,] 2.170332  0.9238553 3.270871 -1.5441517 2.466412  2.79953282
[3,] 3.214988  0.8554705 3.535641 -0.1205533 3.057674  0.53514690
[4,] 2.112973 -0.3915936 3.574049  0.1224274 2.816445  0.02743305
[5,] 3.875925  0.1835678 4.237681 -1.1217373 4.015698 -1.19018925
[6,] 4.492317 -1.3624362 3.605374 -0.7925712 1.541456  0.70211275
         [,7]        [,8]     [,9]      [,10]
[1,] 2.412597 -0.68667529 2.693478  0.5390070
[2,] 3.499522 -0.57118915 2.724884 -0.3781316
[3,] 3.164220 -0.80261049 5.139696  0.3642769
[4,] 1.781402 -0.02899568 3.702517 -0.4853577
[5,] 3.435076  0.71499645 2.017988 -0.7940890
[6,] 2.561281  1.31073192 1.372816 -0.4561410

# 添加行名和列名
colnames(test) = paste("test", 1:10, sep = '')
rownames(test) = paste("gene", 1:20, sep = '')
head(test)
         test1      test2    test3      test4    test5       test6
gene1 5.862158  1.0025110 4.643383  1.3927443 3.078703 -0.70255575
gene2 2.170332  0.9238553 3.270871 -1.5441517 2.466412  2.79953282
gene3 3.214988  0.8554705 3.535641 -0.1205533 3.057674  0.53514690
gene4 2.112973 -0.3915936 3.574049  0.1224274 2.816445  0.02743305
gene5 3.875925  0.1835678 4.237681 -1.1217373 4.015698 -1.19018925
gene6 4.492317 -1.3624362 3.605374 -0.7925712 1.541456  0.70211275
         test7       test8    test9     test10
gene1 2.412597 -0.68667529 2.693478  0.5390070
gene2 3.499522 -0.57118915 2.724884 -0.3781316
gene3 3.164220 -0.80261049 5.139696  0.3642769
gene4 1.781402 -0.02899568 3.702517 -0.4853577
gene5 3.435076  0.71499645 2.017988 -0.7940890
gene6 2.561281  1.31073192 1.372816 -0.4561410

# 直接画图，设置行高和列的树高 
pheatmap(test, treeheight_row = 50, treeheight_col = 50)

# 取消列聚类，并且更改颜色
pheatmap(test, cluster_cols = FALSE, 
         color = colorRampPalette(c("green","white","red"))(1000))

# 增加分组信息，使得pheatmap显示行或列的分组信息
# 增加Time，CellType分组信息
annotation_col = data.frame(CellType = factor(rep(c("X1", "X2"), 5)), 
                            Time = 1:5)  
rownames(annotation_col) = paste("test", 1:10, sep = "") 
# 增加GeneClass分组信息
annotation_row = data.frame(GeneClass = factor(rep(c("P1", "P2", "P3"), 
                                                   c(10, 7, 3))))  

rownames(annotation_row) = paste("gene", 1:20, sep = "") 

# 画图
pheatmap(test, annotation_col = annotation_col, annotation_row = annotation_row)

## 使用annotation_colors参数设定各个分组的颜色  
ann_colors <- list(Time = c("white", "green"),
                   cellType = c(X1= "#1B9E77", X2 = "#D95F02"),
                   GeneClass = c(P1 = "#7570B3", P2 = "#E7298A", P3 = "#66A61E"))   

# 画图
pheatmap(test, annotation_col = annotation_col,
         annotation_row = annotation_row, 
         annotation_colors = ann_colors) 

# 使用cut_rows,和cut_cols可以根据行列的聚类数据将热图分隔开
pheatmap(test, annotation_col = annotation_col,
         annotation_row = annotation_row, 
         annotation_colors = ann_colors,
         cutree_rows = 3,cutree_cols = 2) 
# 数字3和2是根据聚类树加载出来的 

# 查看pheatmap的聚类结果
> result <- pheatmap(test)
> summary(result)
         Length Class  Mode   
tree_row 7      hclust list   
tree_col 7      hclust list   
kmeans   1      -none- logical
gtable   6      gtable list

标签：10,--,pheatmap,col,test,热图,annotation,row
来源： https://blog.csdn.net/m0_53514218/article/details/120103352