This server allows the use of two packages developped in our
for the R software:
The documentation of the ade4 package is available here, and the documentation of the seqinr package is available here. Note that on this server, these two packages are automatically loaded each time R is launched, so you do not need to use the library(ade4) and library(seqinr) commands (but using them will not hurt).
To run Rweb just type the code you want to execute into the text window below and then click on the submit button. You will get a new html page with the text output of your code followed by the graphical output (if any) from your code. A detailed example of use is here. It shows how you can use this system to search sequence data banks for gene sequences, compute the codon frequences for these genes, and perform a correspondence analysis of this data table.
You can try examples from the ade4 package by just clicking the Submit button with the examples below. Just remove these lines to type your own code. The computer time for all of this is donated by the PBIL. Please note that all actions are logged and that abuse will lead to exclusion of IP addresses.
Here are a few examples of use of Rweb. You can copy the following code examples and paste them into the text window at the top of this page. Alternatively, if you are in a more contemplative mood, you can go to this page and just click and watch.
data(doubs) # load the doubs data set dudi1 <- dudi.pca(doubs$env, scale = TRUE, scan = FALSE, nf = 3) # first PCA dudi2 <- dudi.pca(doubs$fish, scale = FALSE, scan = FALSE, nf = 2) # second PCA coin1 <- coinertia(dudi1,dudi2, scan = FALSE, nf = 2) # coinertia analysis coin1 # display coinertia analysis results plot(coin1) # plot coinertia results r1 <- randtest(coin1) # perform the permutation test r1 # display it plot(r1) # plot it
choosebank(bank = "emglib") # chooose the emglib sequence bank req <- query("liste", "sp=mycoplasma genitalium et t=cds") # search the bank for sequences seqs <- lapply(liste$req, getSequence) # retrieve the sequences tabco <- lapply(seqs, uco) # compute codon frequences tabco <- as.data.frame(lapply(tabco, as.vector), row.names = names(tabco[])) # make the dataframe names(tabco) <- liste$req # setup codon names ca <- dudi.coa(tabco, scan = F, nf = 3) # perform CA s.label(ca$co, clabel = 0, sub = "Genes F1xF2 map") # plot Genes map s.label(ca$li, sub = "Codons F1xF2 map") # plot codons map
mat <- dist.alignment(aln, matrix = "similarity") dst <- lingoes(mat) cat <- as.factor(c(1,1,1,1,2,3,3,3,3,3,3,3,4,1,1,1,1,1,1,1,1,3,3,3,3,5,5,5,3)) pco <- dudi.pco(dst, scan = F, nf = 3) s.label(pco$li, sub = "F1xF2 map") s.chull(pco$li, cat, optchull=1, add.plot=TRUE, col=c("red","black","green","purple","blue"))
X # check that the data table has been read correctly pca1=dudi.pca(X,scan=F) # do the PCA of this table scatter(pca1) # PCA biplot s.corcircle(pca1$co) # PCA correlarion circle s.label(pca1$li) # PCA row scores score(pca1) # PCA canonical display : variables = f(principal components)
x <- rnorm(100) # 100 random numbers from a normal(0,1) distribution y <- exp(x) + rnorm(100) # an exponential function with error result <- lsfit(x,y) # regress x on y and store the results ls.print(result) # print the regression results plot(x,y) # pretty obvious what this does abline(result) # add the regression line to the plot lines(lowess(x,y), col=2) # add a nonparametric regression line (a smoother) hist(result$residuals) # histogram of the residuals from the regression