annotate PCA_plot.R @ 9:2e8c9032e8d8 draft

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author mb2013
date Tue, 20 May 2014 03:27:01 -0400
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1 #Plottool makes a graph of Principal Components created with a Principal Component analysis.
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2 #MB
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3
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4 #commands extracting of commandline
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5 args <- commandArgs(TRUE)
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6
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7 #input files and options
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8 input <- args[1]
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9 main_title <- args[2]
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10 x_title <- args[3]
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11 y_title <- args[4]
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12 x_column <- args[5]
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13 y_column <- args[6]
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14 names <- args [7] #name of every sample in one file
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15 #output file
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16 output <- args[8]
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17
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18 suppressMessages(library("geomorph")) #package geomorph
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19
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20 #reading of input files
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21 read <- read.csv(file <- input,header = TRUE)
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22 read2 <- scan(file <- names, what = "", quiet = TRUE)
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23
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24 pca1 <- read[,as.integer(x_column)] #principal component
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25 pca2 <- read[,as.integer(y_column)] #principal component
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26
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27 png(output) #output in png format
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28
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29 #axis boundaries
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30 minpca1 = min(pca1) - max(pca1)
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31 maxpca1 = max(pca1) + max(pca1)
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32 minpca2 = min(pca2) - max(pca2)
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33 maxpca2 = max(pca2) + max(pca2)
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34
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35 #creating the plot with principal components and titels
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36 suppressMessages(plot(pca1,pca2, main = main_title, xlab = x_title, ylab = y_title, pch=20,cex=0.6, xlim = c(minpca1,maxpca1), ylim=c(minpca2,maxpca2)))
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37 #add labels to data points
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38 text(pca1,pca2,labels = read2, pos = 2, cex = 0.7,col = heat.colors(35:40))
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39
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40 graphics.off()