17. tSNE图绘制
清除当前环境中的变量
rm(list=ls())
设置工作目录
setwd("C:/Users/Dell/Desktop/R_Plots/17tsne/")
查看示例数据
head(iris)
## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa
使用tsne包进行tSNE降维可视化分析
# 加载tsne包
library(tsne)
colors = rainbow(length(unique(iris$Species)))
names(colors) = unique(iris$Species)
head(colors)
## setosa versicolor virginica
## "#FF0000FF" "#00FF00FF" "#0000FFFF"
# 使用tsne函数进行tSNE降维分析
tsne_iris = tsne(iris[,1:4],k=2,perplexity=50)
## sigma summary: Min. : 0.565012665854053 |1st Qu. : 0.681985646004023 |Median : 0.713004330336136 |Mean : 0.716213420895748 |3rd Qu. : 0.74581655363904 |Max. : 0.874979764925049 |
## Epoch: Iteration #100 error is: 12.4789319339631
## Epoch: Iteration #200 error is: 0.228964032001554
## Epoch: Iteration #300 error is: 0.225993664440037
## Epoch: Iteration #400 error is: 0.181661747509326
## Epoch: Iteration #500 error is: 0.181628530267247
## Epoch: Iteration #600 error is: 0.181628527460257
## Epoch: Iteration #700 error is: 0.181628527460191
## Epoch: Iteration #800 error is: 0.181628527460191
## Epoch: Iteration #900 error is: 0.181628527460191
## Epoch: Iteration #1000 error is: 0.181628527460191
# 查看tSNE降维后的结果
head(tsne_iris)
## [,1] [,2]
## [1,] 3.2389639 5.779896
## [2,] 3.1307192 3.026691
## [3,] 1.9806959 4.528267
## [4,] 0.6876072 4.567498
## [5,] 2.5127031 6.712823
## [6,] 3.5102694 7.830132
# 使用基础plot函数可视化tSNE降维后的结果
plot(tsne_iris,col=colors[iris$Species],pch=16,
xlab = "tSNE_1",ylab = "tSNE_2",main = "tSNE plot")
# 添加分隔线
abline(h=0,v=0,lty=2,col="gray")
# 添加图例
legend("topright",title = "Species",inset = 0.01,
legend = unique(iris$Species),pch=16,
col = unique(colors[iris$Species]))
image.png
使用Rtsne包进行tSNE降维可视化分析
# 加载Rtsne包
library(Rtsne)
iris_unique <- unique(iris) # Remove duplicates
iris_matrix <- as.matrix(iris_unique[,1:4])
head(iris_matrix)
## Sepal.Length Sepal.Width Petal.Length Petal.Width
## 1 5.1 3.5 1.4 0.2
## 2 4.9 3.0 1.4 0.2
## 3 4.7 3.2 1.3 0.2
## 4 4.6 3.1 1.5 0.2
## 5 5.0 3.6 1.4 0.2
## 6 5.4 3.9 1.7 0.4
# Set a seed if you want reproducible results
set.seed(42)
# 使用Rtsne函数进行tSNE降维分析
tsne_out <- Rtsne(iris_matrix,pca=FALSE,dims=2,
perplexity=30,theta=0.0) # Run TSNE
head(tsne_out)
## $N
## [1] 149
##
## $Y
## [,1] [,2]
## [1,] 5.5067790 24.486797
## [2,] 7.6737152 23.079876
## [3,] 8.1913796 23.761097
## [4,] 8.4254467 23.808179
## [5,] 5.5960403 24.921692
## [6,] 3.4308904 24.959199
## [7,] 8.1381015 24.557058
## [8,] 6.1900369 24.173668
## [9,] 9.0993792 23.285372
## [10,] 7.4001506 23.500007
## [11,] 3.9869935 24.736220
## [12,] 7.0156526 24.518013
## [13,] 8.0765386 23.166514
## [14,] 9.4908994 23.556526
## [15,] 2.8235240 25.573620
## [16,] 2.9130954 25.935506
## [17,] 3.6599753 25.426126
## [18,] 5.4125340 24.349586
## [19,] 3.0210424 24.869039
## [20,] 4.6411079 25.257085
## [21,] 4.2922549 23.641746
## [22,] 4.8038395 24.901788
## [23,] 7.8765660 21.926248
## [24,] 5.4954619 23.046120
## [25,] 7.1852574 25.092759
## [26,] 7.2300950 22.976782
## [27,] 5.7752939 23.532759
## [28,] 4.9904034 24.266867
## [29,] 5.3041970 24.031600
## [30,] 7.7778841 24.221787
## [31,] 7.6319477 23.823039
## [32,] 4.4463848 23.708368
## [33,] 3.9290986 25.893639
## [34,] 3.2745094 25.857022
## [35,] 7.3529093 23.516745
## [36,] 6.7761239 23.210195
## [37,] 4.0294992 24.106827
## [38,] 6.0324266 25.059326
## [39,] 9.1217786 23.534852
## [40,] 5.7711840 24.044706
## [41,] 5.9092367 24.569308
## [42,] 9.3613002 22.569342
## [43,] 9.0282188 24.001022
## [44,] 5.2225531 23.159773
## [45,] 3.4211933 24.162079
## [46,] 8.0525016 23.160908
## [47,] 4.6038886 25.326149
## [48,] 8.4113423 24.034475
## [49,] 4.2748166 24.781044
## [50,] 6.4893044 23.869299
## [51,] -3.4834923 -9.965512
## [52,] -1.9690760 -10.664461
## [53,] -3.8191676 -10.383389
## [54,] 3.1376088 -10.703924
## [55,] -2.4532383 -10.770603
## [56,] 0.9900592 -11.432847
## [57,] -2.2628047 -11.362811
## [58,] 4.5040714 -9.968530
## [59,] -2.5192007 -10.292370
## [60,] 3.2323610 -11.120233
## [61,] 4.4280067 -10.256122
## [62,] 0.5648119 -10.757219
## [63,] 2.3575941 -9.270542
## [64,] -1.1990073 -11.613762
## [65,] 2.9143236 -9.675958
## [66,] -2.4854425 -9.950649
## [67,] 0.9030404 -11.725632
## [68,] 1.9350463 -10.053143
## [69,] -0.5092066 -13.138326
## [70,] 2.8690573 -10.213814
## [71,] -2.2482343 -12.499771
## [72,] 0.9718897 -9.969406
## [73,] -1.9907939 -13.095569
## [74,] -0.8307111 -11.551066
## [75,] -1.2508515 -10.232772
## [76,] -2.1673565 -10.101118
## [77,] -3.3901546 -10.456176
## [78,] -4.2762412 -11.213459
## [79,] -0.4942210 -11.332514
## [80,] 3.3653166 -9.570109
## [81,] 3.3298577 -10.238496
## [82,] 3.5141631 -10.036563
## [83,] 2.2275955 -10.048487
## [84,] -3.1599068 -13.331195
## [85,] 1.3926380 -11.951350
## [86,] -1.5899534 -11.566936
## [87,] -3.1257502 -10.387121
## [88,] -0.3192176 -12.900888
## [89,] 1.6793000 -10.887428
## [90,] 2.8248768 -10.725630
## [91,] 2.1670620 -11.425018
## [92,] -1.0644772 -11.285936
## [93,] 2.2230720 -10.159786
## [94,] 4.4493154 -9.976223
## [95,] 2.0909366 -11.002940
## [96,] 1.3450678 -10.767548
## [97,] 1.4286380 -10.861575
## [98,] -0.4859747 -10.529980
## [99,] 4.5531760 -9.766885
## [100,] 1.8256231 -10.622254
## [101,] -8.1681708 -15.141821
## [102,] -3.0821655 -14.054047
## [103,] -9.0429357 -13.408360
## [104,] -6.2913266 -12.856362
## [105,] -7.7669167 -13.505570
## [106,] -10.4974274 -13.294026
## [107,] 2.6560764 -12.368364
## [108,] -9.8999107 -12.946700
## [109,] -7.6408393 -12.392806
## [110,] -9.6394676 -14.323147
## [111,] -5.9640846 -14.026406
## [112,] -5.4826524 -13.139493
## [113,] -7.5860668 -13.644660
## [114,] -2.8719220 -14.392450
## [115,] -3.5419594 -14.708240
## [116,] -6.7399088 -14.578607
## [117,] -6.4682393 -13.069037
## [118,] -10.6462356 -14.040398
## [119,] -10.8093339 -13.034725
## [120,] -1.6239970 -13.770771
## [121,] -8.4482533 -14.022786
## [122,] -2.7673819 -14.345725
## [123,] -10.6205472 -13.101836
## [124,] -3.4751382 -12.775843
## [125,] -8.1144685 -13.985993
## [126,] -9.3980637 -13.136582
## [127,] -3.0073132 -12.651019
## [128,] -2.8873036 -12.760297
## [129,] -6.9566725 -13.128339
## [130,] -9.1876614 -12.729380
## [131,] -9.8001932 -12.948082
## [132,] -10.6301905 -14.035993
## [133,] -7.0797403 -13.260803
## [134,] -3.7329453 -12.449749
## [135,] -4.9349389 -12.667315
## [136,] -10.1578052 -13.475241
## [137,] -7.5054287 -15.084694
## [138,] -6.3581608 -13.135133
## [139,] -2.5507459 -12.699941
## [140,] -7.5576427 -13.885089
## [141,] -7.8477888 -14.288949
## [142,] -6.9311661 -14.331769
## [143,] -8.6350440 -14.108707
## [144,] -8.2340074 -14.614955
## [145,] -6.8427060 -14.201949
## [146,] -3.8694359 -13.229102
## [147,] -6.0352962 -13.720744
## [148,] -6.9420702 -15.132527
## [149,] -3.0791891 -13.529181
##
## $costs
## [1] 1.179324e-04 -3.689469e-04 -1.472921e-04 -4.273294e-04 7.624380e-04
## [6] -1.313372e-04 1.126943e-03 7.574498e-04 -1.913122e-04 -6.932105e-04
## [11] -8.492889e-05 1.024120e-03 -4.320760e-04 9.484793e-05 1.034310e-04
## [16] -1.842527e-05 -2.769062e-04 3.582073e-05 9.299024e-05 3.510226e-04
## [21] 1.186597e-04 9.862502e-05 2.806501e-03 7.798399e-04 7.986599e-04
## [26] -1.339566e-04 9.950297e-04 1.161829e-04 -7.778344e-05 -2.067769e-05
## [31] -6.404696e-04 -5.211731e-05 6.500397e-05 1.041786e-05 -3.733109e-04
## [36] 2.337047e-04 -3.017156e-04 9.792827e-04 -1.755743e-04 3.190899e-04
## [41] 2.431256e-04 1.020450e-04 2.787914e-04 7.182304e-04 1.113900e-03
## [46] -3.749718e-04 3.334247e-04 8.082786e-05 -5.599858e-05 7.585540e-04
## [51] 7.531620e-04 7.173614e-04 2.078562e-03 -1.084888e-05 1.999274e-03
## [56] 1.693053e-03 7.959443e-04 4.374940e-04 1.132554e-03 7.866948e-04
## [61] 3.883855e-05 1.778366e-03 1.568548e-03 2.732406e-03 4.332908e-05
## [66] 6.904889e-04 2.165244e-03 2.403076e-04 2.726784e-03 -3.148891e-05
## [71] 1.613799e-03 2.174618e-03 3.656731e-03 1.439881e-03 2.410020e-03
## [76] 1.061569e-03 1.028394e-03 5.288050e-03 3.202629e-03 2.366342e-04
## [81] 8.124443e-05 1.351984e-04 3.548746e-04 1.674996e-03 1.447612e-03
## [86] 4.387094e-04 1.335889e-03 3.588848e-03 -1.542536e-04 1.726962e-04
## [91] 5.801623e-04 2.254048e-03 4.292584e-04 3.809488e-04 6.045353e-04
## [96] 1.324231e-04 6.585140e-04 3.054234e-03 8.623991e-04 4.056875e-04
## [101] 4.624438e-04 1.366814e-03 1.009017e-03 1.713772e-03 6.288805e-04
## [106] 1.608018e-04 1.268004e-03 3.481348e-04 1.375940e-03 5.179650e-04
## [111] 3.086146e-03 3.631408e-03 8.518228e-04 9.834214e-04 1.469104e-03
## [116] 1.224737e-03 1.777474e-03 1.681800e-04 5.902857e-05 2.021059e-03
## [121] 4.853900e-04 2.000830e-03 1.299285e-04 2.299773e-03 3.225243e-04
## [126] 4.490196e-04 1.750803e-03 1.941641e-03 1.081639e-03 8.451969e-04
## [131] 4.045783e-04 1.189270e-04 6.152785e-04 2.858092e-03 1.208743e-03
## [136] -1.943961e-04 8.259299e-04 1.318010e-03 1.952109e-03 5.434035e-04
## [141] 1.365092e-04 -1.536645e-04 6.192905e-04 2.872638e-04 2.285592e-04
## [146] 1.959709e-03 3.139510e-03 1.286250e-03 1.493989e-03
##
## $itercosts
## [1] 45.0879058 43.7584671 45.1243240 44.7692647 43.7384615 0.3317077
## [7] 0.1473289 0.1354032 0.1325056 0.1308674 0.1299235 0.1293005
## [13] 0.1288420 0.1284922 0.1282276 0.1280100 0.1278221 0.1276655
## [19] 0.1275252 0.1274040
##
## $origD
## [1] 4
##
## $perplexity
## [1] 30
# Show the objects in the 2D tsne representation
plot(tsne_out$Y,col=iris_unique$Species, asp=1,pch=20,
xlab = "tSNE_1",ylab = "tSNE_2",main = "tSNE plot")
# 添加分隔线
abline(h=0,v=0,lty=2,col="gray")
# 添加图例
legend("topright",title = "Species",inset = 0.01,
legend = unique(iris_unique$Species),pch=16,
col = unique(iris_unique$Species))
image.png
# data.frame as input
set.seed(42)
# 更改perplexity和theta值
tsne_out <- Rtsne(iris_unique,pca=FALSE,
perplexity = 20,theta=0.5)
head(tsne_out)
## $N
## [1] 149
##
## $Y
## [,1] [,2]
## [1,] 7.715389 17.705174
## [2,] 6.714282 13.769817
## [3,] 8.157742 14.124667
## [4,] 7.809143 13.697934
## [5,] 7.987368 17.528455
## [6,] 6.791518 20.153909
## [7,] 8.710824 14.416845
## [8,] 7.376314 16.792163
## [9,] 8.189728 12.791145
## [10,] 6.717610 14.309527
## [11,] 7.618207 19.807624
## [12,] 6.979341 15.679252
## [13,] 7.061598 13.611695
## [14,] 8.703919 12.799065
## [15,] 7.369231 21.314902
## [16,] 6.842204 21.427915
## [17,] 7.166539 20.435873
## [18,] 7.465803 17.763868
## [19,] 7.676051 20.613974
## [20,] 6.710571 19.156099
## [21,] 8.443834 19.008168
## [22,] 6.742755 18.731957
## [23,] 9.501581 15.070496
## [24,] 5.980155 17.128316
## [25,] 6.139254 15.733237
## [26,] 6.217819 14.178264
## [27,] 6.428262 17.065069
## [28,] 7.724756 18.336525
## [29,] 7.976838 18.004713
## [30,] 7.424925 14.529141
## [31,] 6.971005 14.358575
## [32,] 8.369564 18.977941
## [33,] 6.326850 20.407753
## [34,] 6.816088 21.083007
## [35,] 6.780623 14.360317
## [36,] 8.060725 15.576010
## [37,] 8.395989 19.619546
## [38,] 8.443099 17.040896
## [39,] 8.452467 12.996652
## [40,] 7.395879 17.298077
## [41,] 8.014606 17.144911
## [42,] 7.481623 12.267588
## [43,] 8.733881 13.378562
## [44,] 5.947488 17.507990
## [45,] 5.787446 19.212730
## [46,] 7.087497 13.571666
## [47,] 6.582526 19.272593
## [48,] 8.207260 13.926048
## [49,] 7.399908 19.469612
## [50,] 7.571307 16.307071
## [51,] 4.394639 -19.289305
## [52,] 4.166886 -20.851062
## [53,] 4.793280 -19.198982
## [54,] 5.602155 -26.671096
## [55,] 5.245131 -20.511082
## [56,] 5.646036 -24.120716
## [57,] 3.872715 -21.206414
## [58,] 5.519235 -28.451264
## [59,] 4.926860 -20.266936
## [60,] 6.062190 -26.537496
## [61,] 5.759777 -28.268192
## [62,] 4.701436 -23.749113
## [63,] 3.679397 -26.426654
## [64,] 5.351755 -21.905942
## [65,] 4.314637 -26.952424
## [66,] 4.202548 -20.080409
## [67,] 5.695679 -23.868457
## [68,] 4.431456 -25.724963
## [69,] 7.270078 -21.688010
## [70,] 4.974752 -26.640891
## [71,] 3.830695 -22.192594
## [72,] 3.994333 -24.489692
## [73,] 6.430228 -21.040284
## [74,] 5.630178 -21.954776
## [75,] 4.817189 -21.140262
## [76,] 4.506897 -20.389368
## [77,] 5.195707 -19.683392
## [78,] 5.153288 -19.294883
## [79,] 5.072630 -22.577081
## [80,] 4.597493 -27.433435
## [81,] 5.193637 -27.056603
## [82,] 5.100150 -27.339082
## [83,] 4.540650 -26.050193
## [84,] 6.254610 -21.319542
## [85,] 6.071711 -24.223658
## [86,] 3.745414 -22.074190
## [87,] 4.578353 -19.769608
## [88,] 7.252855 -21.728829
## [89,] 5.034584 -24.916893
## [90,] 5.501645 -26.377054
## [91,] 5.984879 -25.268674
## [92,] 5.017774 -22.007440
## [93,] 4.644536 -26.005433
## [94,] 5.515568 -28.395657
## [95,] 5.434407 -25.395652
## [96,] 4.942550 -24.676220
## [97,] 5.057557 -24.744289
## [98,] 4.794154 -22.178158
## [99,] 5.288612 -28.548037
## [100,] 4.924180 -25.288019
## [101,] -14.720190 8.120475
## [102,] -11.835778 3.096062
## [103,] -12.778642 9.733934
## [104,] -13.188997 5.671674
## [105,] -13.631634 7.611333
## [106,] -12.901432 11.506110
## [107,] -11.673101 1.879873
## [108,] -12.440571 10.837229
## [109,] -11.274141 7.140769
## [110,] -13.912300 10.122575
## [111,] -12.147243 6.270212
## [112,] -12.546087 5.270368
## [113,] -12.749814 7.888978
## [114,] -11.901891 2.760847
## [115,] -11.288509 2.772746
## [116,] -13.928568 6.939219
## [117,] -12.943740 6.008282
## [118,] -13.810883 11.574257
## [119,] -12.670568 11.882708
## [120,] -12.875627 3.311425
## [121,] -13.261832 8.806721
## [122,] -11.580872 2.758515
## [123,] -12.723562 11.649209
## [124,] -12.087709 4.196538
## [125,] -13.394957 8.403919
## [126,] -12.508938 10.198705
## [127,] -11.790559 4.043863
## [128,] -11.473917 4.019978
## [129,] -13.212842 6.489570
## [130,] -12.149792 10.078319
## [131,] -12.451790 10.727172
## [132,] -13.817733 11.547700
## [133,] -13.307234 6.693915
## [134,] -12.403537 4.451978
## [135,] -13.428587 4.452829
## [136,] -13.025346 11.051335
## [137,] -14.547148 7.493264
## [138,] -13.066118 5.920086
## [139,] -11.374131 3.779877
## [140,] -12.558457 8.064209
## [141,] -13.482160 8.119613
## [142,] -12.168193 7.623663
## [143,] -13.578190 8.961591
## [144,] -14.001458 8.494630
## [145,] -12.462676 7.298402
## [146,] -12.411605 4.054358
## [147,] -12.344027 6.129648
## [148,] -14.553464 7.009208
## [149,] -11.499951 3.587173
##
## $costs
## [1] 6.242687e-04 6.293003e-04 7.580414e-04 1.967870e-04 1.029160e-03
## [6] 3.812285e-04 1.284010e-03 1.947316e-03 5.602282e-05 2.609845e-04
## [11] 1.131349e-03 2.355685e-03 1.496672e-04 -2.725965e-04 8.615276e-05
## [16] -9.103645e-05 -1.026418e-04 7.471934e-04 -4.820882e-05 1.225569e-03
## [21] 1.401053e-04 1.238423e-03 1.083713e-03 1.013981e-03 4.054493e-04
## [26] 4.235251e-04 1.866602e-03 1.260274e-03 8.423138e-04 8.466582e-04
## [31] 1.028833e-05 4.868350e-04 4.055505e-04 4.395368e-04 7.272476e-04
## [36] 1.526546e-03 4.291155e-04 1.168867e-03 2.936939e-05 1.256397e-03
## [41] 3.652797e-04 -2.487728e-04 4.364336e-04 4.936336e-04 6.284857e-04
## [46] 1.242948e-04 9.720837e-04 7.300661e-04 1.276410e-03 2.595590e-03
## [51] -1.638248e-05 9.963329e-04 2.082263e-04 -1.539286e-04 1.250050e-03
## [56] 1.445096e-03 1.478114e-03 5.981239e-06 3.058988e-04 4.631793e-04
## [61] -1.839881e-04 1.833061e-03 2.231296e-03 1.756529e-03 2.905429e-04
## [66] 2.668615e-04 1.168726e-03 1.302897e-04 2.269873e-03 3.376048e-04
## [71] 1.177970e-03 3.345636e-03 1.446172e-03 6.378471e-04 1.522122e-03
## [76] 2.028485e-04 8.296849e-05 4.347997e-04 2.332496e-03 -5.056151e-05
## [81] 1.733452e-04 1.506508e-04 8.317497e-04 4.218776e-04 3.149415e-04
## [86] 9.216793e-04 3.308710e-04 3.635962e-03 -1.450625e-04 4.038861e-04
## [91] 5.577626e-04 1.774802e-03 8.720726e-04 4.305382e-05 8.922485e-04
## [96] -6.292440e-05 8.183549e-04 2.754186e-03 -8.058446e-05 8.772215e-04
## [101] 1.208109e-04 7.315875e-04 2.038605e-03 1.421054e-03 1.149629e-03
## [106] 5.391937e-04 2.029174e-03 7.924317e-04 3.640921e-03 1.011342e-03
## [111] 1.994893e-03 2.035141e-03 1.417985e-03 6.375140e-04 6.554719e-04
## [116] 2.277190e-03 1.273799e-03 5.490530e-04 8.494791e-05 2.960040e-04
## [121] 1.175569e-03 7.489057e-04 3.251306e-04 2.266579e-04 5.750585e-04
## [126] 9.387132e-04 4.593413e-06 7.492131e-04 1.387842e-03 9.528573e-04
## [131] 8.688057e-04 4.153938e-04 7.817248e-04 1.251807e-05 7.458750e-04
## [136] -9.347800e-06 8.179079e-04 6.716049e-04 2.656127e-04 8.893953e-04
## [141] 4.275921e-04 -1.101141e-04 1.378859e-03 6.152032e-04 6.984994e-04
## [146] 1.646995e-04 2.326887e-03 1.450425e-03 4.802579e-04
##
## $itercosts
## [1] 47.1165208 45.6090433 45.3971807 45.3865836 44.1171993 0.4610884
## [7] 0.1465717 0.1383095 0.1329804 0.1320085 0.1276541 0.1273532
## [13] 0.1252604 0.1228743 0.1209324 0.1210714 0.1220934 0.1225643
## [19] 0.1223175 0.1226974
##
## $origD
## [1] 7
##
## $perplexity
## [1] 20
# 使用ggplot2包可视化tSNE降维的结果
library(ggplot2)
tsne_res <- as.data.frame(tsne_out$Y)
colnames(tsne_res) <- c("tSNE1","tSNE2")
head(tsne_res)
## tSNE1 tSNE2
## 1 7.715389 17.70517
## 2 6.714282 13.76982
## 3 8.157742 14.12467
## 4 7.809143 13.69793
## 5 7.987368 17.52846
## 6 6.791518 20.15391
# 使用ggplot2可视化tSNE降维的结果
ggplot(tsne_res,aes(tSNE1,tSNE2,color=iris_unique$Species)) +
geom_point() + theme_bw() +
geom_hline(yintercept = 0,lty=2,col="red") +
geom_vline(xintercept = 0,lty=2,col="blue",lwd=1) +
theme(plot.title = element_text(hjust = 0.5)) +
labs(title = "tSNE plot",color="Species")
image.png
sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=Chinese (Simplified)_China.936
[2] LC_CTYPE=Chinese (Simplified)_China.936
[3] LC_MONETARY=Chinese (Simplified)_China.936
[4] LC_NUMERIC=C
[5] LC_TIME=Chinese (Simplified)_China.936
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] scatterplot3d_0.3-41 ggplot2_3.2.0 Rtsne_0.15
[4] tsne_0.1-3
loaded via a namespace (and not attached):
[1] Rcpp_1.0.5 pillar_1.4.2 compiler_3.6.0 tools_3.6.0
[5] digest_0.6.20 evaluate_0.14 tibble_2.1.3 gtable_0.3.0
[9] lattice_0.20-38 pkgconfig_2.0.2 rlang_0.4.7 Matrix_1.2-17
[13] rstudioapi_0.10 yaml_2.2.0 xfun_0.8 withr_2.1.2
[17] dplyr_0.8.3 knitr_1.23 grid_3.6.0 tidyselect_0.2.5
[21] glue_1.3.1 R6_2.4.0 rmarkdown_1.13 irlba_2.3.3
[25] purrr_0.3.2 magrittr_1.5 scales_1.0.0 htmltools_0.3.6
[29] rsconnect_0.8.16 assertthat_0.2.1 colorspace_1.4-1 labeling_0.3
[33] lazyeval_0.2.2 munsell_0.5.0 crayon_1.3.4
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