在科學(xué)論文中汰扭，我們經(jīng)常要用到熱圖抚吠。我們在熱圖在單細胞數(shù)據(jù)分析中的應(yīng)用比較系統(tǒng)地介紹了熱圖的一般規(guī)則德崭。但是在實際操作中還是會遇到一些細節(jié)問題斥黑，如標簽順序。
我們知道一個好的熱圖接癌，要能反映出數(shù)據(jù)規(guī)律心赶，直覺上就是要有明顯的色塊。那么色塊是如何來的呢缺猛？和行與列的順序有關(guān)缨叫。如一張好的熱圖大概率是這樣的：
但是如果我們調(diào)整順序椭符，他可以變成這樣的：
對我們來說重要的是獲得這個順序，然后指定給繪圖函數(shù)耻姥。我們以熟悉的pheatmap為例來探索一下销钝。首先生成示例數(shù)據(jù)：
library(pheatmap)
# Create test matrix
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) = c(paste("CGene", 6:10, sep = ""),
paste("AGene", 1:5, sep = ""),
paste("BGene", 11:15, sep = ""),
paste("DGene", 16:20, sep = ""))
看看數(shù)據(jù)長什么樣子：
test
Test1 Test2 Test3 Test4 Test5 Test6
CGene6 3.32676462 -2.16507595 4.232450403 -0.73583213 3.94062305 -0.1935842619
CGene7 3.30040713 0.08865765 3.721572091 0.33449053 2.73292952 0.4583932832
CGene8 1.50450030 0.64337406 3.407904162 -1.24057682 3.14174263 -0.0007014311
CGene9 2.89088634 -0.55950670 2.060130582 -1.75583323 1.07926694 2.2556162284
CGene10 1.90369857 1.40255666 1.760750107 -0.76906325 2.64811141 -0.6957942691
AGene1 3.42061019 1.14950064 4.268530703 0.05037557 1.84633305 0.5137683525
AGene2 2.88919835 -1.02100837 2.957415715 -1.09980021 3.67011986 0.7510053428
AGene3 5.24239748 0.02736920 4.045355782 -0.08883342 4.06748687 -0.9685845021
AGene4 2.19006433 1.37861550 2.337982108 -0.94394769 3.83553785 -0.8334859349
AGene5 4.48235967 -1.48192686 5.028429364 0.15901242 3.49067895 0.5836504001
BGene11 0.93281128 0.60297065 0.877725891 2.68570163 -0.52096014 0.5303119758
BGene12 -0.82352032 4.13015350 -0.007314182 2.56230292 -1.22882126 2.0095278472
BGene13 1.07999506 2.00713092 1.185458666 1.13050138 0.15584559 2.3795046412
BGene14 1.11955349 2.84165755 0.220021162 1.63569739 0.99095614 3.3335572441
BGene15 1.77628153 6.37128696 1.004835310 4.90696601 0.75322787 5.3301565398
DGene16 0.04400472 6.33588183 -1.293469424 5.43806241 0.53726670 6.2000870073
DGene17 -2.63598249 7.79111111 -0.204355079 6.85814507 -0.87600545 6.8738334335
DGene18 -0.48197063 6.21941112 0.841207756 6.19352280 0.12741642 6.0838277426
DGene19 0.96229006 5.79064015 1.319576057 7.18360581 -0.05522554 5.6089813401
DGene20 -1.42032585 4.29067156 0.589306112 5.99965957 0.43606552 5.7949180143
Test7 Test8 Test9 Test10
CGene6 4.138778102 1.6304399 1.4972186 0.6664516
CGene7 4.205202621 0.7133720 1.3688061 0.4749147
CGene8 3.675146838 -0.8371708 2.4173558 -0.8573423
CGene9 0.911284470 0.6367740 1.8973446 0.5885573
CGene10 2.381027675 2.0743930 3.6874262 1.1493406
AGene1 3.416045270 -0.0662255 2.1358439 -1.3471116
AGene2 4.091088541 0.2684579 3.6841199 -1.7729912
AGene3 2.746024503 0.3570507 2.2417769 -0.1226907
AGene4 2.734958681 -0.7147136 1.8119604 -0.9273917
AGene5 2.131046458 0.5774685 4.1504215 -1.0478849
BGene11 0.367833875 1.5309153 -1.0897623 3.3879448
BGene12 0.003437035 1.1982992 -1.1184832 1.2544010
BGene13 0.124903765 2.0180698 -1.1180846 4.0343573
BGene14 -1.623291426 2.4192553 -1.3206414 0.7060437
BGene15 0.576155533 7.4567201 1.3057335 5.6594995
DGene16 0.542256420 5.8187826 -1.6232905 7.1829024
DGene17 -0.711543153 7.1164359 -0.8563482 7.9621794
DGene18 0.632542083 5.9143762 -0.9905354 7.6225081
DGene19 -0.659880146 5.0144296 -0.5088869 4.9703428
DGene20 -0.445718763 4.8705198 -1.5070905 6.2237708
默認參數(shù)：
p1 <- pheatmap(test, main = "pheatmap")
這時的順序是按聚類順序來的。
p2 <- pheatmap((test),cluster_row = FALSE,main = "cluster_row = FALSE")
不聚類時琐簇，行順序就是我們的輸入矩陣的數(shù)據(jù)順序蒸健。
我們把行名按字母排個序。
p3<- pheatmap(test[order(rownames(test)),],cluster_row = FALSE,main = 'test[order(rownames(test)),]\ncluster_row = FALSE')
這時候就是字母序了婉商。
有時候似忧，我們只想留下聚類結(jié)果，并不想展示聚類軸丈秩，怎么辦呢盯捌？
nr=rownames(test)[p1$tree_row[["order"]]]
nr # 可以把這個順序傳遞給Doheatmap
[1] "DGene17" "DGene16" "DGene18" "BGene15" "DGene19" "DGene20" "BGene11" "BGene13"
[9] "BGene12" "BGene14" "CGene6" "AGene3" "AGene5" "CGene8" "AGene4" "AGene2"
[17] "CGene7" "AGene1" "CGene9" "CGene10"
nc=colnames(test)[p1$tree_col[["order"]]]
p4<-pheatmap(test[nr,nc], main = "pheatmap/nremove cluster lable",cluster_rows = F)
最后，我們把這四張圖拼在一起蘑秽，對讀著有個交代饺著。
require(ggplotify)
p1 = as.ggplot(p1)
p2 = as.ggplot(p2)
p3 = as.ggplot(p3)
p4 = as.ggplot(p4)
p12 <-cowplot:: plot_grid(p1, p2, labels = c('A', 'B'), align = 'h',
rel_widths = c(1, 1.3))
p34 = cowplot::plot_grid(p3, p4, labels = c('C', 'D'), align = 'h',
rel_widths = c(1, 1.3))
comb = cowplot::plot_grid(p12,p34, ncol = 1,
rel_heights = c(1, 1))
DoHeatmap clustering specific genes and not top x genes #2261
繼續(xù)來看pheatmap那些有趣的事情
熱圖如何去掉聚類樹的同時保留聚類的順序？
【r<-ggplot2】cowplot在網(wǎng)格中排列圖形
Arranging plots in a grid
https://github.com/satijalab/seurat/issues/2222
