modelLINEAR_CROSS: Constant for 'Matrix_eQTL_engine'.

In andreyshabalin/MatrixEQTL: Matrix eQTL: Ultra Fast eQTL Analysis via Large Matrix Operations

Constant for Matrix_eQTL_engine.

Description

Set parameter useModel = modelLINEAR_CROSS in the call of Matrix_eQTL_main to indicate that Matrix eQTL should include the interaction of SNP and the last covariate in the model and test for its significance.

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 = 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$pvalue;
rez = c(beta = beta, tstat = tstat, pvalue = pvalue)
# And compare to those from the linear regression in R
{
    cat('\n\n Matrix eQTL: \n'); 
    print(rez);
    cat('\n R summary(lm()) output: \n')
    lmodel = lm( gene.mat ~ snps.mat + cvrt.mat + snps.mat*cvrt.mat[,nc], 
                    weights = 1/noise.std^2);
    lmout = tail(summary(lmodel)$coefficients,1)[, c("Estimate", "t value", "Pr(>|t|)")];
    print( tail(lmout) );
}

# Results from Matrix eQTL and 'lm' must agree
stopifnot(all.equal(lmout, rez, check.attributes = FALSE));

andreyshabalin/MatrixEQTL documentation built on Oct. 1, 2023, 12:40 a.m.