modelLINEAR {MatrixEQTL} | R Documentation |
Constant for Matrix_eQTL_engine
.
Description
Set parameter useModel = modelLINEAR
in the call of
Matrix_eQTL_main
to indicate that the effect of
genotype on expression should be assumed to be additive linear.
References
The package website: http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL/
See Also
See Matrix_eQTL_engine
for reference and sample code.
Examples
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$new( t(cvrt.mat) );
# name of temporary output file
filename = tempfile();
# Call the main analysis function
me = Matrix_eQTL_main(
snps = snps1,
gene = gene1,
cvrt = cvrt1,
output_file_name = filename,
pvOutputThreshold = 1,
useModel = modelLINEAR,
errorCovariance = diag(noise.std^2),
verbose = TRUE,
pvalue.hist = FALSE );
# remove the output file
unlink( filename );
# Pull Matrix eQTL results - t-statistic and p-value
beta = me$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('\n\n Matrix eQTL: \n');
print(rez);
cat('\n R summary(lm()) output: \n');
lmodel = lm( gene.mat ~ snps.mat + cvrt.mat, weights = 1/noise.std^2 );
lmout = summary(lmodel)$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));
[Package MatrixEQTL version 2.3 Index]