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1 library("MatrixEQTL");
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2
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3 anova.groups = 5;
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4 options(MatrixEQTL.ANOVA.categories = anova.groups);
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5
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6 # Number of columns (samples)
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7 n = 100;
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8 # Number of covariates
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9 nc = 10;
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10 # Generate the standard deviation of the noise
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11 noise.std = 0.1 + rnorm(n)^2;
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12 # Generate the covariates
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13 cvrt.mat = 2 + matrix(rnorm(n*nc), ncol = nc);
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14
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15 # Generate the vectors with single genotype and expression variables
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16 snps.mat = floor(runif(n, min = 0, max = anova.groups));
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17 gene.mat = 1 + (snps.mat==1) + cvrt.mat %*% rnorm(nc) + rnorm(n) * noise.std;
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18
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19 # Create 3 SlicedData objects for the analysis
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20 snps1 = SlicedData$new( matrix( snps.mat, nrow = 1 ) );
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21 gene1 = SlicedData$new( matrix( gene.mat, nrow = 1 ) );
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22 cvrt1 = SlicedData$new( t(cvrt.mat) );
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23
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24 # Produce no output files
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25 filename = NULL; # tempfile()
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26
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27 # Call the main analysis function
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28 me = Matrix_eQTL_main(
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29 snps = snps1,
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30 gene = gene1,
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31 cvrt = cvrt1,
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32 output_file_name = filename,
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33 pvOutputThreshold = 1,
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34 useModel = modelANOVA,
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35 errorCovariance = diag(noise.std^2),
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36 verbose = TRUE,
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37 pvalue.hist = FALSE );
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38
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39 # Pull Matrix eQTL results - t-statistic and p-value
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40
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41 fstat = me$all$eqtls$statistic;
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42 pvalue = me$all$eqtls$pvalue;
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43 rez = c( Fstat = fstat, pvalue = pvalue);
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44 # And compare to those from ANOVA in R
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45 {
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46 cat("\n\n Matrix eQTL: \n");
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47 print(rez);
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48 cat("\n R anova(lm()) output: \n");
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49 lmdl = lm( gene.mat ~ cvrt.mat + factor(snps.mat),
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50 weights = 1/noise.std^2 );
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51 lmout = anova(lmdl)[2, c("F value","Pr(>F)")];
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52 print( lmout );
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53 }
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54
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55 # Results from Matrix eQTL and "lm" must agree
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56 stopifnot(all.equal(lmout, rez, check.attributes=FALSE));
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