Labelling in Principal component analysis in R

Question

This is a continuation of the following post.

how to color points in 17 colors based on principal component?

This is as.fumeric function 

as.fumeric <- function(x,levels=unique(x)) as.numeric(factor(x,levels=levels))


cols=as.fumeric(gtex_pm$tissue)
plot(pc_gtex$x[,1],pc_gtex$x[,2],col=cols, main = "PCA", xlab = "PC1", ylab = "PC2")
legend("topleft", col=1:17, legend = paste(unique(gtex_pm$tissue), 1:17), pch = 20, bty='n', cex=1.5)

Once i have the plot with 17 levels,the legend created displays 17 levels,but the colors for them repeat after 1 to 8.So the 9th label has the same color as the first. Also,Is there any better way to add the group labels on the PCA plot.I have 17 unique groups.Either 2 groups are being assigned the same color because of "cols" variable or because of plotting "legend".The levels in cols are 17 .

I can not post the picture due to a low reputation.The details of the data are in the linked post pasted above.

This is the input data:

   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 group table:

head(gtex_pm)

     sample   tissue

   1 SRR1069514 Prostate
   2 SRR1071717  Bladder
   3 SRR1073069 Prostate
   4 SRR1074410 Prostate

I'm going to make the exact same comment as your last question (because i don't see how this is different): It's much easier to help you if you provide a [reproducible example](http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example) (which includes sample input data so we can run your code). Plus it's not easy choosing 17 different colors that are easily distinguishable. You might want to consult a graphic artist for that. The colors for base plots when you numeric `col=` values come form `palette()`. See the documentation for that function — MrFlick, Jun 01 '15 at 21:36
I had pasted the input data in my last post,and I am pasting it here again. — user45292, Jun 01 '15 at 22:20

Labelling in Principal component analysis in R

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