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comparison MatrixEQTL/man/modelLINEAR_CROSS.Rd @ 0:cd4c8e4a4b5b draft

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author jasonxu
date Fri, 12 Mar 2021 08:12:46 +0000
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1 \name{modelLINEAR_CROSS}
2 \alias{modelLINEAR_CROSS}
3 \docType{data}
4 \title{
5 Constant for \code{\link{Matrix_eQTL_engine}}.
6 }
7 \description{
8 Set parameter \code{useModel = modelLINEAR_CROSS} in the call of \code{\link{Matrix_eQTL_main}} to indicate that Matrix eQTL should include the interaction of SNP and last covariate in the model and test for its significance.
9 }
10 \examples{
11 library('MatrixEQTL')
12
13 # Number of columns (samples)
14 n = 25;
15
16 # Number of covariates
17 nc = 10;
18
19 # Generate the standard deviation of the noise
20 noise.std = 0.1 + rnorm(n)^2;
21
22 # Generate the covariates
23 cvrt.mat = 2 + matrix(rnorm(n*nc), ncol = nc);
24
25 # Generate the vectors with single genotype and expression variables
26 snps.mat = cvrt.mat \%*\% rnorm(nc) + rnorm(n);
27 gene.mat = cvrt.mat \%*\% rnorm(nc) + rnorm(n) * noise.std +
28 1 + 0.5 * snps.mat + snps.mat * cvrt.mat[,nc];
29
30 # Create 3 SlicedData objects for the analysis
31 snps1 = SlicedData$new( matrix( snps.mat, nrow = 1 ) );
32 gene1 = SlicedData$new( matrix( gene.mat, nrow = 1 ) );
33 cvrt1 = SlicedData$new( t(cvrt.mat) );
34
35 # name of temporary output file
36 filename = tempfile();
37
38 # Call the main analysis function
39 me = Matrix_eQTL_main(
40 snps = snps1,
41 gene = gene1,
42 cvrt = cvrt1,
43 output_file_name = filename,
44 pvOutputThreshold = 1,
45 useModel = modelLINEAR_CROSS,
46 errorCovariance = diag(noise.std^2),
47 verbose = TRUE,
48 pvalue.hist = FALSE );
49 # remove the output file
50 unlink( filename );
51
52 # Pull Matrix eQTL results - t-statistic and p-value
53 beta = me$all$eqtls$beta;
54 tstat = me$all$eqtls$statistic;
55 pvalue = me$all$eqtls$pvalue;
56 rez = c(beta = beta, tstat = tstat, pvalue = pvalue)
57 # And compare to those from the linear regression in R
58 {
59 cat('\n\n Matrix eQTL: \n');
60 print(rez);
61 cat('\n R summary(lm()) output: \n')
62 lmodel = lm( gene.mat ~ snps.mat + cvrt.mat + snps.mat*cvrt.mat[,nc],
63 weights = 1/noise.std^2 );
64 lmout = tail(summary( lmodel )$coefficients,1)[,c(1,3,4)];
65 print( tail(lmout) );
66 }
67
68 # Results from Matrix eQTL and 'lm' must agree
69 stopifnot(all.equal(lmout, rez, check.attributes=FALSE))
70 }
71 \references{
72 The package website: \url{http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL/}
73 }
74 \seealso{
75 See \code{\link{Matrix_eQTL_engine}} for reference and sample code.
76 }