--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/MatrixEQTL/demo/e.interaction.r	Fri Mar 12 08:20:57 2021 +0000
@@ -0,0 +1,56 @@
+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$new( t(cvrt.mat) );
+
+# Produce no output files
+filename = NULL; # tempfile()
+
+# Call the main analysis function
+me = Matrix_eQTL_main(
+	snps = snps1, 
+	gene = gene1, 
+	cvrt = cvrt1, 
+	output_file_name = filename, 
+	pvOutputThreshold = 1, 
+	useModel = modelLINEAR_CROSS, 
+	errorCovariance = diag(noise.std^2), 
+	verbose = TRUE,
+	pvalue.hist = FALSE );
+
+# Pull Matrix eQTL results - t-statistic and p-value
+beta = me$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 + snps.mat*cvrt.mat[,nc],
+					   weights = 1/noise.std^2 );
+	lmout = tail(summary(lmdl)$coefficients,1)[,c(1,3,4)];
+	print( tail(lmout) );
+}
+
+# Results from Matrix eQTL and "lm" must agree
+stopifnot(all.equal(lmout, rez, check.attributes=FALSE));