modelANOVA: Constant for 'Matrix_eQTL_engine'.

In MatrixEQTL: Matrix eQTL: Ultra Fast eQTL Analysis via Large Matrix Operations

Description

Set parameter useModel = modelANOVA in the call of Matrix_eQTL_main to indicate that the genotype should be treated as a categorical variable.

Note

By default, the number of ANOVA categories is fixed to be 3.

To set it to a different number (say, 4) use the following command:

options(MatrixEQTL.ANOVA.categories=4)

To check the current settings run:

getOption("MatrixEQTL.ANOVA.categories", 3);

References

The package website: http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL/

See Also

See Matrix_eQTL_engine for reference and sample code.

Examples

library("MatrixEQTL")    
            
# Number of columns (samples)
n = 100;

# 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 = floor(runif(n, min = 0, max = 3));
gene.mat = 1 + (snps.mat==1) + cvrt.mat %*% rnorm(nc) + rnorm(n) * noise.std;

# 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) );

# name of temporary output file
filename = tempfile();

snps1
gene1

# Call the main analysis function
me = Matrix_eQTL_main(
    snps = snps1, 
    gene = gene1, 
    cvrt = cvrt1, 
    output_file_name = filename, 
    pvOutputThreshold = 1, 
    useModel = modelANOVA, 
    errorCovariance = diag(noise.std^2), 
    verbose = TRUE,
    pvalue.hist = FALSE );
# remove the output file
unlink( filename );

# Pull Matrix eQTL results - t-statistic and p-value

fstat = me$all$eqtls$statistic;
pvalue = me$all$eqtls$pvalue;
rez = c( Fstat = fstat, pvalue = pvalue)
# And compare to those from ANOVA in R
{
    cat("\n\n Matrix eQTL: \n"); 
    print(rez);
    cat("\n R anova(lm()) output: \n")
    lmodel = lm( gene.mat ~ cvrt.mat + factor(snps.mat), weights = 1/noise.std^2);
    lmout = anova(lmodel)[2, c("F value", "Pr(>F)")];
    print( lmout )
}

# Results from Matrix eQTL and "lm" must agree
stopifnot(all.equal(lmout, rez, check.attributes = FALSE));

MatrixEQTL documentation built on Dec. 22, 2019, 5:06 p.m.