Matrix eQTL
test code for heteroskedastic
linear regression with covariates
library("MatrixEQTL");
# Number of columns (samples)
n = 100;
# 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 genotype and expression variables
snps.mat = cvrt.mat %*% rnorm(nc) + rnorm(n);
gene.mat = cvrt.mat %*% rnorm(nc) + rnorm(n) * noise.std + 0.5 * snps.mat + 1;
# 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$new( t(cvrt.mat) );
# Produce no output files
filename = NULL; # tempfile()
# Call the main analysis function
me = Matrix_eQTL_main(
snps = snps1,
gene = gene1,
cvrt = cvrt1,
output_file_name = filename,
pvOutputThreshold = 1,
useModel = modelLINEAR,
errorCovariance = diag(noise.std^2),
verbose = TRUE,
pvalue.hist = FALSE );
# Pull Matrix eQTL results - t-statistic and p-value
beta = me$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("\n\n Matrix eQTL: \n");
print(rez);
cat("\n R summary(lm()) output: \n");
lmdl = lm( gene.mat ~ snps.mat + cvrt.mat,
weights = 1/noise.std^2 );
lmout = summary(lmdl)$coefficients[2,c("Estimate","t value","Pr(>|t|)")];
print( lmout );
}
# Results from Matrix eQTL and "lm" must agree
stopifnot(all.equal(lmout, rez, check.attributes=FALSE));