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comparison MatrixEQTL/demo/d.ANOVA5.r @ 0:cd4c8e4a4b5b draft

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author jasonxu
date Fri, 12 Mar 2021 08:12:46 +0000
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-1:000000000000 0:cd4c8e4a4b5b
1 library("MatrixEQTL");
2
3 anova.groups = 5;
4 options(MatrixEQTL.ANOVA.categories = anova.groups);
5
6 # Number of columns (samples)
7 n = 100;
8 # Number of covariates
9 nc = 10;
10 # Generate the standard deviation of the noise
11 noise.std = 0.1 + rnorm(n)^2;
12 # Generate the covariates
13 cvrt.mat = 2 + matrix(rnorm(n*nc), ncol = nc);
14
15 # Generate the vectors with single genotype and expression variables
16 snps.mat = floor(runif(n, min = 0, max = anova.groups));
17 gene.mat = 1 + (snps.mat==1) + cvrt.mat %*% rnorm(nc) + rnorm(n) * noise.std;
18
19 # Create 3 SlicedData objects for the analysis
20 snps1 = SlicedData$new( matrix( snps.mat, nrow = 1 ) );
21 gene1 = SlicedData$new( matrix( gene.mat, nrow = 1 ) );
22 cvrt1 = SlicedData$new( t(cvrt.mat) );
23
24 # Produce no output files
25 filename = NULL; # tempfile()
26
27 # Call the main analysis function
28 me = Matrix_eQTL_main(
29 snps = snps1,
30 gene = gene1,
31 cvrt = cvrt1,
32 output_file_name = filename,
33 pvOutputThreshold = 1,
34 useModel = modelANOVA,
35 errorCovariance = diag(noise.std^2),
36 verbose = TRUE,
37 pvalue.hist = FALSE );
38
39 # Pull Matrix eQTL results - t-statistic and p-value
40
41 fstat = me$all$eqtls$statistic;
42 pvalue = me$all$eqtls$pvalue;
43 rez = c( Fstat = fstat, pvalue = pvalue);
44 # And compare to those from ANOVA in R
45 {
46 cat("\n\n Matrix eQTL: \n");
47 print(rez);
48 cat("\n R anova(lm()) output: \n");
49 lmdl = lm( gene.mat ~ cvrt.mat + factor(snps.mat),
50 weights = 1/noise.std^2 );
51 lmout = anova(lmdl)[2, c("F value","Pr(>F)")];
52 print( lmout );
53 }
54
55 # Results from Matrix eQTL and "lm" must agree
56 stopifnot(all.equal(lmout, rez, check.attributes=FALSE));