library("MatrixEQTL")

# Number of samples
n = 100;

# Number of variables
ngs = 2000;

# Common signal in all variables (population stratification)
pop = 0.2 * rnorm(n);

# data matrices
snps.mat = matrix(rnorm(n*ngs), ncol = ngs) + pop;
gene.mat = matrix(rnorm(n*ngs), ncol = ngs) + pop + snps.mat*((1:ngs)/ngs)^9/2;

# data objects for Matrix eQTL engine
snps1 = SlicedData$new( t( snps.mat ) );
gene1 = SlicedData$new( t( gene.mat ) );
cvrt1 = SlicedData$new();
rm(snps.mat, gene.mat);

# Slice data in blocks of 500 variables
snps1$ResliceCombined(500);
gene1$ResliceCombined(500);

# Produce no output files
filename = NULL; # tempfile()

# Perform analysis recording information for a histogram
me = Matrix_eQTL_main(
	snps = snps1,
	gene = gene1,
	cvrt = cvrt1,
	output_file_name = filename, 
	pvOutputThreshold = 1e-100, 
	useModel = modelLINEAR, 
	errorCovariance = numeric(), 
	verbose = TRUE,
	pvalue.hist = 100);

# png(filename = "histogram.png", width = 650, height = 650);
plot(me, col="grey");
# dev.off();