使用pheatmap包绘制热图

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1. 加载所需R包

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library(pheatmap)

2. 设置工作路径

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setwd("/Users/Davey/Desktop/VennDiagram/")
# 清除当前环境中的变量
rm(list=ls())

3. 构建测试数据集

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test = matrix(rnorm(200), 20, 10)
test[1:10, seq(1, 10, 2)] = test[1:10, seq(1, 10, 2)] + 3
test[11:20, seq(2, 10, 2)] = test[11:20, seq(2, 10, 2)] + 2
test[15:20, seq(2, 10, 2)] = test[15:20, seq(2, 10, 2)] + 4
colnames(test) = paste("Test", 1:10, sep = "")
rownames(test) = paste("Gene", 1:20, sep = "")
head(test)
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##          Test1      Test2    Test3      Test4    Test5       Test6
## Gene1 4.064973  0.7535271 3.024070 -2.1294440 4.407945 -0.35677097
## Gene2 2.360043  1.6974946 3.273425 -2.3341406 3.839523  0.16982944
## Gene3 3.253465 -0.9011582 1.716257 -0.2294471 4.636610 -0.24520382
## Gene4 4.070226 -0.6191941 3.734437  1.9348314 4.426825 -0.17730957
## Gene5 3.821414  0.5584876 1.871479 -0.2784607 2.633761  0.01332901
## Gene6 3.012469  0.1738285 3.652423 -2.0083435 4.124951 -0.67899611
##          Test7      Test8    Test9        Test10
## Gene1 3.602764  1.2903843 2.044119  1.826159e+00
## Gene2 3.083160  0.2642755 2.855381  1.988289e-01
## Gene3 3.417809 -0.1362079 3.858884 -8.390304e-01
## Gene4 2.911934  0.4299550 4.128398 -3.011521e+00
## Gene5 2.651758 -1.6884728 3.001079  1.861780e+00
## Gene6 1.934270  0.5811059 2.297763  6.878644e-05
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# 默认绘图
pheatmap(test)

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# scale = "row"参数对行进行归一化
pheatmap(test, scale = "row")

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# clustering_method参数设定不同聚类方法，默认为"complete",可以设定为'ward', 'ward.D', 'ward.D2', 'single', 'complete', 'average', 'mcquitty', 'median' or 'centroid'
pheatmap(test,scale = "row", clustering_method = "average")

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# clustering_distance_rows = "correlation"参数设定行聚类距离方法为Pearson corralation，默认为欧氏距离"euclidean"
pheatmap(test, scale = "row", clustering_distance_rows = "correlation")

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# color参数自定义颜色
pheatmap(test, color = colorRampPalette(c("navy", "white", "firebrick3"))(50))

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# cluster_row = FALSE参数设定不对行进行聚类
pheatmap(test, cluster_row = FALSE)

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# legend_breaks参数设定图例显示范围，legend_labels参数添加图例标签
pheatmap(test, legend_breaks = c(1:5), legend_labels = c("1.0","2.0","3.0","4.0","5.0"))

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# legend = FALSE参数去掉图例
pheatmap(test, legend = FALSE)

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# border_color参数设定每个热图格子的边框色
pheatmap(test, border_color = "red")

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# border=FALSE参数去掉边框线
pheatmap(test, border=FALSE)

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# show_rownames和show_colnames参数设定是否显示行名和列名
pheatmap(test,show_rownames=F,show_colnames=F)

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# treeheight_row和treeheight_col参数设定行和列聚类树的高度，默认为50
pheatmap(test, treeheight_row = 30, treeheight_col = 50)

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# display_numbers = TRUE参数设定在每个热图格子中显示相应的数值，number_color参数设置数值字体的颜色
pheatmap(test, display_numbers = TRUE,number_color = "blue")

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# number_format = "%.1e"参数设定数值的显示格式
pheatmap(test, display_numbers = TRUE, number_format = "%.1e")

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# 自定义数值的显示方式
pheatmap(test, display_numbers = matrix(ifelse(test > 5, "*", ""), nrow(test)))

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# cellwidth和cellheight参数设定每个热图格子的宽度和高度，main参数添加主标题
pheatmap(test, cellwidth = 15, cellheight = 12, main = "Example heatmap")

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# 构建列注释信息
annotation_col = data.frame(
  CellType = factor(rep(c("CT1", "CT2"), 5)), 
  Time = 1:5
)
rownames(annotation_col) = paste("Test", 1:10, sep = "")
head(annotation_col)
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##       CellType Time
## Test1      CT1    1
## Test2      CT2    2
## Test3      CT1    3
## Test4      CT2    4
## Test5      CT1    5
## Test6      CT2    1
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# 构建行注释信息
annotation_row = data.frame(
  GeneClass = factor(rep(c("Path1", "Path2", "Path3"), c(10, 4, 6)))
)
rownames(annotation_row) = paste("Gene", 1:20, sep = "")
head(annotation_row)
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##       GeneClass
## Gene1     Path1
## Gene2     Path1
## Gene3     Path1
## Gene4     Path1
## Gene5     Path1
## Gene6     Path1
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# annotation_col参数添加列注释信息
pheatmap(test, annotation_col = annotation_col)

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# annotation_legend = FALSE参数去掉注释图例
pheatmap(test, annotation_col = annotation_col, annotation_legend = FALSE)

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# annotation_col和annotation_row参数同时添加行和列的注释信息
pheatmap(test, annotation_row = annotation_row, annotation_col = annotation_col)

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# 自定注释信息的颜色列表
ann_colors = list(
  Time = c("white", "firebrick"),
  CellType = c(CT1 = "#1B9E77", CT2 = "#D95F02"),
  GeneClass = c(Path1 = "#7570B3", Path2 = "#E7298A", Path3 = "#66A61E")
)
head(ann_colors)
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## $Time
## [1] "white"     "firebrick"
## 
## $CellType
##       CT1       CT2 
## "#1B9E77" "#D95F02" 
## 
## $GeneClass
##     Path1     Path2     Path3 
## "#7570B3" "#E7298A" "#66A61E"
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# annotation_colors设定注释信息的颜色
pheatmap(test, annotation_col = annotation_col, annotation_colors = ann_colors, main = "Title")

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

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pheatmap(test, annotation_col = annotation_col, annotation_colors = ann_colors[2])

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# gaps_row = c(10, 14)参数在第10和14行处添加gap, 要求对行不进行聚类
pheatmap(test, annotation_col = annotation_col, cluster_rows = FALSE, gaps_row = c(10, 14))

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# cutree_col = 2参数将列按聚类树的结果分成两部分, 要求对列进行聚类
pheatmap(test, annotation_col = annotation_col, cluster_rows = FALSE, gaps_row = c(10, 14), cutree_col = 2)

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# 对行和列都不聚类，自定义划分行和列的gap
pheatmap(test, annotation_col = annotation_col, cluster_rows = FALSE, cluster_cols = FALSE, gaps_row = c(6, 10, 14), gaps_col = c(2, 5, 8))

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# 自定义行的标签名
labels_row = c("", "", "", "", "", "", "", "", "", "", "", "", "", "", "", 
               "", "", "Il10", "Il15", "Il1b")
# labels_row参数添加行标签
pheatmap(test, annotation_col = annotation_col, labels_row = labels_row)

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# 自定义聚类的距离方法
drows = dist(test, method = "minkowski")
dcols = dist(t(test), method = "minkowski")
# clustering_distance_rows和clustering_distance_cols参数设定行和列的聚类距离方法
pheatmap(test, clustering_distance_rows = drows, clustering_distance_cols = dcols)

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# fontsize参数设定标签字体大小，filename参数设定图片保存名称
pheatmap(test, cellwidth = 15, cellheight = 12, fontsize = 8, filename = "test.pdf")

4. 将热图结果按聚类后的顺序输出

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aa=pheatmap(test,scale="row")  #热图，归一化，并聚类

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# 简要查看热图对象的信息
summary(aa)
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##          Length Class  Mode   
## tree_row 7      hclust list   
## tree_col 7      hclust list   
## kmeans   1      -none- logical
## gtable   6      gtable list
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order_row = aa$tree_row$order  #记录热图的行排序
order_col = aa$tree_col$order    #记录热图的列排序
datat = data.frame(test[order_row,order_col])   # 按照热图的顺序，重新排原始数据
datat = data.frame(rownames(datat),datat,check.names =F)  # 将行名加到表格数据中
colnames(datat)[1] = "geneid" 
write.table(datat,file="reorder.txt",row.names=FALSE,quote = FALSE,sep='\t')  #输出结果，按照热图中的顺序
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sessionInfo()
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## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: OS X El Capitan 10.11.3
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] zh_CN.UTF-8/zh_CN.UTF-8/zh_CN.UTF-8/C/zh_CN.UTF-8/zh_CN.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] pheatmap_1.0.10
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_0.12.18       digest_0.6.16      rprojroot_1.3-2   
##  [4] grid_3.5.1         gtable_0.2.0       backports_1.1.2   
##  [7] magrittr_1.5       scales_1.0.0       evaluate_0.11     
## [10] stringi_1.2.4      rmarkdown_1.10     RColorBrewer_1.1-2
## [13] tools_3.5.1        stringr_1.3.1      munsell_0.5.0     
## [16] yaml_2.2.0         compiler_3.5.1     colorspace_1.3-2  
## [19] htmltools_0.3.6    knitr_1.20

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