问题
I am doing PCA in R on a data frame(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)
Below is my group table for the PCA.The sample column in this table actually represents the rows of the main data to be plotted.The columns of that table are genes.So basically I have 17 groups/tissues to be represented on PCA.
head(gtex_pm)
sample tissue
1 SRR1069514 Prostate
2 SRR1071717 Bladder
3 SRR1073069 Prostate
Based on the above gtex_group object looks like the levels:
head(gtex_group)
[1] 1 2 1 1 1
THE sample head of Main table for PCA is :The rownames are the Samples
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
The figure eventually represents 8 colors and then repeats itself,so we cant distinguish between some tissues.I want to show 17 different colors.How do I do that?
回答1:
It's hard to say without knowing exactly what your data look like, but perhaps something like this would work:
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")
来源：https://stackoverflow.com/questions/30405247/how-to-color-points-in-17-colors-based-on-principal-component