获取pheatmap聚类后和标准化后的结果

mat <- matrix(rnorm(30), nrow=5)

colnames(mat) <- paste("sample", 1:6, sep="_")

rownames(mat) <- paste("gene", 1:5, sep="_")

mat

##          sample_1   sample_2   sample_3    sample_4   sample_5   sample_6
## gene_1 -0.3286368  0.3153119 -0.7730821 -0.85242874 -0.5303812  0.5088226
## gene_2 -1.3153020  0.3193550  0.4496518 -1.08782734  1.7620763 -0.9312810
## gene_3  0.6545161 -0.8220414 -1.1916559  0.04775437  0.2814619  1.8720241
## gene_4  1.0810986  0.2298092 -0.3615045  0.70162614  1.8572989  0.7250737
## gene_5 -1.8931573  2.7013864  0.5049798 -0.13541785 -1.7796036 -0.3185864

library(pheatmap)

# 绘图同时存储绘图结果
(a <- pheatmap(mat, cluster_rows = T, cluster_cols = T))

# 查看绘图数据的结构
# 直接查看会很大，这里只展示其前2层
# str: structure
str(a, max.level = 2)

# Rstudio中
# View(a)

## List of 4
##  $ tree_row:List of 7
##   ..$ merge      : int [1:4, 1:2] -1 -4 -2 -5 -3 1 2 3
##   ..$ height     : num [1:4] 2.4 3.21 4.38 5.56
##   ..$ order      : int [1:5] 5 2 4 1 3
##   ..$ labels     : chr [1:5] "gene_1" "gene_2" "gene_3" "gene_4" ...
##   ..$ method     : chr "complete"
##   ..$ call       : language hclust(d = d, method = method)
##   ..$ dist.method: chr "euclidean"
##   ..- attr(*, "class")= chr "hclust"
##  $ tree_col:List of 7
##   ..$ merge      : int [1:5, 1:2] -1 -6 -2 -5 3 -4 1 -3 2 4
##   ..$ height     : num [1:5] 1.98 2.29 2.55 3.78 5.21
##   ..$ order      : int [1:6] 2 3 5 6 1 4
##   ..$ labels     : chr [1:6] "sample_1" "sample_2" "sample_3" "sample_4" ...
##   ..$ method     : chr "complete"
##   ..$ call       : language hclust(d = d, method = method)
##   ..$ dist.method: chr "euclidean"
##   ..- attr(*, "class")= chr "hclust"
##  $ kmeans  : logi NA
##  $ gtable  :List of 6
##   ..$ grobs        :List of 6
##   ..$ layout       :'data.frame':    6 obs. of  7 variables:
##   ..$ widths       :List of 6
##   .. ..- attr(*, "class")= chr [1:2] "unit.list" "unit"
##   ..$ heights      :List of 5
##   .. ..- attr(*, "class")= chr [1:2] "unit.list" "unit"
##   ..$ respect      : logi FALSE
##   ..$ rownames     : NULL
##   ..- attr(*, "class")= chr [1:4] "gtable" "gTree" "grob" "gDesc"
##  - attr(*, "class")= chr "pheatmap"

mat_cluster <- mat[a$tree_row$order, a$tree_col$order]

mat_cluster

##          sample_2   sample_3   sample_5   sample_6   sample_1    sample_4
## gene_5  2.7013864  0.5049798 -1.7796036 -0.3185864 -1.8931573 -0.13541785
## gene_2  0.3193550  0.4496518  1.7620763 -0.9312810 -1.3153020 -1.08782734
## gene_4  0.2298092 -0.3615045  1.8572989  0.7250737  1.0810986  0.70162614
## gene_1  0.3153119 -0.7730821 -0.5303812  0.5088226 -0.3286368 -0.85242874
## gene_3 -0.8220414 -1.1916559  0.2814619  1.8720241  0.6545161  0.04775437

(a <- pheatmap(mat, scale="row", display_numbers = T))

mat_scale <- round(t(apply(mat, 1, scale)),2)
colnames(mat_scale) <- colnames(mat)
mat_scale

mat_cluster <- mat_scale[a$tree_row$order, a$tree_col$order]

mat_cluster

##        sample_2 sample_3 sample_5 sample_6 sample_1 sample_4
## gene_3    -0.88    -1.22     0.13     1.58     0.47    -0.08
## gene_4    -0.63    -1.42     1.53     0.03     0.50    -0.01
## gene_2     0.38     0.49     1.59    -0.67    -0.99    -0.80
## gene_1     1.04    -0.87    -0.45     1.38    -0.09    -1.01
## gene_5     1.69     0.39    -0.96    -0.10    -1.03     0.01