如何根据主成分对 17 种颜色的点进行着色？
how to color points in 17 colors based on principal component?
我正在 R 中对数据框进行 PCA(df_f)
pc_gtex <- prcomp(df_f)
plot(pc_gtex$x[,1], pc_gtex$x[,2], col=gtex_group, main = "PCA", xlab = "PC1", ylab = "PC2")
legend("topleft", col=1:17, legend = paste(unique(gtex_pm$tissue), 1:17), pch = 20, bty='n', cex=1.5)
下面是我的组table，这个PCA.The样本列在这个table实际上代表了主要数据的行是那个[=的plotted.The列32=] 是 genes.So 基本上我有 17 groups/tissues 要在 PCA 上表示。
head(gtex_pm)
sample tissue
1 SRR1069514 Prostate
2 SRR1071717 Bladder
3 SRR1073069 Prostate
基于以上gtex_group对象看起来像关卡：
head(gtex_group)
[1] 1 2 1 1 1
PCA 的 Main table 样本头是：行名是样本
SRR1069514 0 0.0009995 5.773065971 1.644998088 0.142367241 0.176471143 0.195566784 0.0009995 0.025667747 3.380994674 1.762502288 0 0.077886539 0 0.002995509 0.01093994 2.110576771 1.38829236 2.26186726 0.431132855 3.108480433 3.96347629 0 0 0.41012092 3.48452699 1.68565794 0 1.425034189 1.87456758 2.590542128 0 0 0 1.941471742 0.961646434 0 1.17711535 0.058268908 0 0.260824618 3.08534443 1.10426296 0.242946179 0.0009995 0 0 0 0.0009995 1.560247668 1.517541898 0.016857117 0.767326579 0.0009995 3.0191069 0 2.607050533 1.446683661 2.288384744 2.62082062 0.19309663 0 0 0.234281296 0 1.415610416 2.328837464 0.008959741 0.911479175 0.375005901 0.660107327 3.184739763 1.16064768 0.001998003 0.138891999 2.219855445 3.1011278 1.81872592 2.98229236 2.4114395 3.24528404 0 1.54734972 0.406131553 0.029558802 0.003992021 0.693647056 2.07581 2.8357982 0.0009995 0.082501222 1.09661029 2.75829962 0.635518068 3.11484775 0.01291623 3.40837159 0
SRR1071717 0 0 0.0009995 4.99519673 1.626491667 0.100749903 0.327863862 0.09531018 0 0.056380333 3.328196489 1.541373182 0 0.091667189 0.044973366 0 0.033434776 1.953311265 1.56444055 1.79142608 0.993622075 3.206236281 3.82609468 0 0 2.565487674 3.2202349 1.1304339 0 1.092258815 1.80203978 2.645394351 0 0 0.0009995 1.681200279 2.047434746 0 0.948176921 0.006975614 0.014888613 0.298622013 2.49667052 1.01884732 0.38662202 0 0 0 0 0.0009995 0.941958479 1.752845376 0.017839918 0.216722984 0.051643233 3.0505518 0 2.034444176 0.988053098 2.235804059 1.89686995 0.090754363 0 0 0.198850859 0 1.585554972 2.274905524 0 0.04305949 0.056380333 0.044016885 0.771496147 1.195436473 0 0.368801124 1.974636427 2.7700856 2.00120969 2.88875935 2.2651947 2.66242502 0 0.429181635 0.04018179 0.034401427 0 0.242161557 1.9907469 2.1384177 0.0009995 0.008959741 0.99916021 2.3892214 0.086177696 3.16821391 0 3.2038434 0
SRR1073069 2.19544522 1.32866525 0.0009995 4.50198508 1.159707388 0.141499562 0.265436464 0.026641931 2.3330173 0.028587457 3.140698044 1.537297235 0.012916225 0.023716527 0 0.002995509 0.049742092 2.071157322 1.02460688 2.11818137 0.359072069 2.419656765 3.5065479 0.137149838 2.121902193 0.305276381 2.95958683 1.49939981 3.14397985 1.001366904 1.450911 1.39475844 1.930071085 1.140074079 0.037295785 1.609437912 0.412109651 0.870456196 0.943516718 0.013902905 0 0.152721087 2.88836976 1.482967248 0.272314595 2.061532121 0.552159487 2.394890764 1.391033116 0.443402947 1.593714952 1.285921387 0.00796817 0.371563556 0.020782539 3.1946651 1.26327891 2.212003715 1.46672161 2.140183804 2.71997877 0.294161039 0.018821754 0.0009995 0.179818427 1.893714192 1.731478538 2.502255288 0.013902905 0.752830183 0.347129531 0.407463111 2.467082065 0.558472277 1.563812734 0.022739487 1.608837732 2.8176816 1.30670988 2.44495233 1.81107178 3.03254625 0.569283193 0.948176921 0.101653654 0.036331929 0 0.786182047 1.9867779 3.5039946 2.463427618 0.008959741 0.76360564 2.20640453 0.514618422 2.87964779 1.11021142 3.18750899 1.22436349
SRR1074410 2.69022562 1.70055751 0.013902905 3.314622273 0.503196597 0.4940863 0.044016885 0.023716527 1.753884517 0.03246719 2.767324893 1.666385193 0.009950331 0.05259245 0 0 0.017839918 1.575260461 0.76779072 2.22202559 0.83377831 2.198113071 3.57953881 0.051643233 2.207284913 0.072320662 3.04414141 1.39177929 2.851746423 0.982452934 1.33210213 1.888583654 1.871340532 1.238664044 0.03246719 1.734659877 0.486737828 0.412109651 1.126551657 0.035367144 0 0.213497174 2.76032635 1.131402111 0.572108852 2.102425378 0.291175962 1.85159947 0.943516718 0.283674051 1.232560261 0.982078472 0 0.223943232 0.035367144 2.9064091 1.583299255 2.376671636 1.185095749 2.07681309 2.20794469 0.877549904 0.151002874 0 0.107059072 3.038312721 1.486365915 2.633829402 0 0.403463105 0.195566784 0.285930539 1.296643139 0.48796633 1.664115474 0.054488185 1.884034745 2.3757426 1.71036863 2.61732284 1.9348492 3.1138708 1.220239777 0.322807874 0.12398598 0.004987542 0.002995509 0.446607051 1.939317 3.8484227 2.78346684 0.025667747 0.78253074 2.03352848 0.181487876 2.7091163 1.00430161 3.1429015 1.24875495
图形最终代表8种颜色然后自己重复，所以我们无法区分一些tissues.I想显示17种不同的colors.How我这样做吗？
如果不知道您的数据究竟是什么样子，很难说，但也许像这样的东西会起作用：
cols <- rainbow(17)[as.factor(gtex_pm$tissue)]
plot(pc_gtex$x[,1], pc_gtex$x[,2], col=cols, main = "PCA", xlab = "PC1", ylab = "PC2")
我正在 R 中对数据框进行 PCA(df_f)
pc_gtex <- prcomp(df_f)
plot(pc_gtex$x[,1], pc_gtex$x[,2], col=gtex_group, main = "PCA", xlab = "PC1", ylab = "PC2")
legend("topleft", col=1:17, legend = paste(unique(gtex_pm$tissue), 1:17), pch = 20, bty='n', cex=1.5)
下面是我的组table，这个PCA.The样本列在这个table实际上代表了主要数据的行是那个[=的plotted.The列32=] 是 genes.So 基本上我有 17 groups/tissues 要在 PCA 上表示。
head(gtex_pm)
sample tissue
1 SRR1069514 Prostate
2 SRR1071717 Bladder
3 SRR1073069 Prostate
基于以上gtex_group对象看起来像关卡：
head(gtex_group)
[1] 1 2 1 1 1
PCA 的 Main table 样本头是：行名是样本
SRR1069514 0 0.0009995 5.773065971 1.644998088 0.142367241 0.176471143 0.195566784 0.0009995 0.025667747 3.380994674 1.762502288 0 0.077886539 0 0.002995509 0.01093994 2.110576771 1.38829236 2.26186726 0.431132855 3.108480433 3.96347629 0 0 0.41012092 3.48452699 1.68565794 0 1.425034189 1.87456758 2.590542128 0 0 0 1.941471742 0.961646434 0 1.17711535 0.058268908 0 0.260824618 3.08534443 1.10426296 0.242946179 0.0009995 0 0 0 0.0009995 1.560247668 1.517541898 0.016857117 0.767326579 0.0009995 3.0191069 0 2.607050533 1.446683661 2.288384744 2.62082062 0.19309663 0 0 0.234281296 0 1.415610416 2.328837464 0.008959741 0.911479175 0.375005901 0.660107327 3.184739763 1.16064768 0.001998003 0.138891999 2.219855445 3.1011278 1.81872592 2.98229236 2.4114395 3.24528404 0 1.54734972 0.406131553 0.029558802 0.003992021 0.693647056 2.07581 2.8357982 0.0009995 0.082501222 1.09661029 2.75829962 0.635518068 3.11484775 0.01291623 3.40837159 0
SRR1071717 0 0 0.0009995 4.99519673 1.626491667 0.100749903 0.327863862 0.09531018 0 0.056380333 3.328196489 1.541373182 0 0.091667189 0.044973366 0 0.033434776 1.953311265 1.56444055 1.79142608 0.993622075 3.206236281 3.82609468 0 0 2.565487674 3.2202349 1.1304339 0 1.092258815 1.80203978 2.645394351 0 0 0.0009995 1.681200279 2.047434746 0 0.948176921 0.006975614 0.014888613 0.298622013 2.49667052 1.01884732 0.38662202 0 0 0 0 0.0009995 0.941958479 1.752845376 0.017839918 0.216722984 0.051643233 3.0505518 0 2.034444176 0.988053098 2.235804059 1.89686995 0.090754363 0 0 0.198850859 0 1.585554972 2.274905524 0 0.04305949 0.056380333 0.044016885 0.771496147 1.195436473 0 0.368801124 1.974636427 2.7700856 2.00120969 2.88875935 2.2651947 2.66242502 0 0.429181635 0.04018179 0.034401427 0 0.242161557 1.9907469 2.1384177 0.0009995 0.008959741 0.99916021 2.3892214 0.086177696 3.16821391 0 3.2038434 0
SRR1073069 2.19544522 1.32866525 0.0009995 4.50198508 1.159707388 0.141499562 0.265436464 0.026641931 2.3330173 0.028587457 3.140698044 1.537297235 0.012916225 0.023716527 0 0.002995509 0.049742092 2.071157322 1.02460688 2.11818137 0.359072069 2.419656765 3.5065479 0.137149838 2.121902193 0.305276381 2.95958683 1.49939981 3.14397985 1.001366904 1.450911 1.39475844 1.930071085 1.140074079 0.037295785 1.609437912 0.412109651 0.870456196 0.943516718 0.013902905 0 0.152721087 2.88836976 1.482967248 0.272314595 2.061532121 0.552159487 2.394890764 1.391033116 0.443402947 1.593714952 1.285921387 0.00796817 0.371563556 0.020782539 3.1946651 1.26327891 2.212003715 1.46672161 2.140183804 2.71997877 0.294161039 0.018821754 0.0009995 0.179818427 1.893714192 1.731478538 2.502255288 0.013902905 0.752830183 0.347129531 0.407463111 2.467082065 0.558472277 1.563812734 0.022739487 1.608837732 2.8176816 1.30670988 2.44495233 1.81107178 3.03254625 0.569283193 0.948176921 0.101653654 0.036331929 0 0.786182047 1.9867779 3.5039946 2.463427618 0.008959741 0.76360564 2.20640453 0.514618422 2.87964779 1.11021142 3.18750899 1.22436349
SRR1074410 2.69022562 1.70055751 0.013902905 3.314622273 0.503196597 0.4940863 0.044016885 0.023716527 1.753884517 0.03246719 2.767324893 1.666385193 0.009950331 0.05259245 0 0 0.017839918 1.575260461 0.76779072 2.22202559 0.83377831 2.198113071 3.57953881 0.051643233 2.207284913 0.072320662 3.04414141 1.39177929 2.851746423 0.982452934 1.33210213 1.888583654 1.871340532 1.238664044 0.03246719 1.734659877 0.486737828 0.412109651 1.126551657 0.035367144 0 0.213497174 2.76032635 1.131402111 0.572108852 2.102425378 0.291175962 1.85159947 0.943516718 0.283674051 1.232560261 0.982078472 0 0.223943232 0.035367144 2.9064091 1.583299255 2.376671636 1.185095749 2.07681309 2.20794469 0.877549904 0.151002874 0 0.107059072 3.038312721 1.486365915 2.633829402 0 0.403463105 0.195566784 0.285930539 1.296643139 0.48796633 1.664115474 0.054488185 1.884034745 2.3757426 1.71036863 2.61732284 1.9348492 3.1138708 1.220239777 0.322807874 0.12398598 0.004987542 0.002995509 0.446607051 1.939317 3.8484227 2.78346684 0.025667747 0.78253074 2.03352848 0.181487876 2.7091163 1.00430161 3.1429015 1.24875495
图形最终代表8种颜色然后自己重复，所以我们无法区分一些tissues.I想显示17种不同的colors.How我这样做吗？
如果不知道您的数据究竟是什么样子，很难说，但也许像这样的东西会起作用：
cols <- rainbow(17)[as.factor(gtex_pm$tissue)]
plot(pc_gtex$x[,1], pc_gtex$x[,2], col=cols, main = "PCA", xlab = "PC1", ylab = "PC2")