pheatmap行标签顺序大调整

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pheatmap行标签顺序大调整

2023-09-25 00:57| 来源: 网络整理| 查看: 265

在科学论文中，我们经常要用到热图。我们在热图在单细胞数据分析中的应用比较系统地介绍了热图的一般规则。但是在实际操作中还是会遇到一些细节问题，如标签顺序。

我们知道一个好的热图，要能反映出数据规律，直觉上就是要有明显的色块。那么色块是如何来的呢？和行与列的顺序有关。如一张好的热图大概率是这样的：

但是如果我们调整顺序，他可以变成这样的：

对我们来说重要的是获得这个顺序，然后指定给绘图函数。我们以熟悉的pheatmap为例来探索一下。首先生成示例数据：

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 = ""))

看看数据长什么样子：

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

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pheatmap行标签顺序大调整

pheatmap行标签顺序大调整

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