library('MatrixEQTL')
# Number of columns (samples)
n = 25;
# Number of covariates
nc = 10;
# Generate the standard deviation of the noise
noise.std = 0.1 + rnorm(n)^2;
# Generate the covariates
cvrt.mat = 2 + matrix(rnorm(n*nc), ncol = nc);
# Generate the vectors with single genotype and expression variables
snps.mat = cvrt.mat %*% rnorm(nc) + rnorm(n);
gene.mat = cvrt.mat %*% rnorm(nc) + rnorm(n) * noise.std +
1 + 0.5 * snps.mat + snps.mat * cvrt.mat[,nc];
# Create 3 SlicedData objects for the analysis
snps1 = SlicedData$new( matrix( snps.mat, nrow = 1 ) );
gene1 = SlicedData$new( matrix( gene.mat, nrow = 1 ) );
cvrt1 = SlicedData$new( t(cvrt.mat) );
# name of temporary output file
filename = tempfile();
# Call the main analysis function
me = Matrix_eQTL_main(
snps = snps1,
gene = gene1,
cvrt = cvrt1,
output_file_name = filename,
pvOutputThreshold = 1,
useModel = modelLINEAR_CROSS,
errorCovariance = diag(noise.std^2),
verbose = TRUE,
pvalue.hist = FALSE );
# remove the output file
unlink( filename );
# Pull Matrix eQTL results - t-statistic and p-value
beta = me$all$eqtls$beta;
tstat = me$all$eqtls$statistic;
pvalue = me$all$eqtls$pvalue;
rez = c(beta = beta, tstat = tstat, pvalue = pvalue)
# And compare to those from the linear regression in R
{
cat('Matrix eQTL:
');
print(rez);
cat('R summary(lm()) output:
')
lmodel = lm( gene.mat ~ snps.mat + cvrt.mat + snps.mat*cvrt.mat[,nc],
weights = 1/noise.std^2 );
lmout = tail(summary( lmodel )$coefficients,1)[,c(1,3,4)];
print( tail(lmout) );
}
# Results from Matrix eQTL and 'lm' must agree
stopifnot(all.equal(lmout, rez, check.attributes=FALSE))
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