library("MatrixEQTL");

# Number of columns (samples)
n = 100;

# Number of covariates
nc = 10;




# 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) + 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 = numeric(), 
	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 );
	
	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));