MatrixEQTL_cis_code {MatrixEQTL}

Sample code for cis/trans-eQTL analysis with Matrix eQTL

Description

The following code is the best starting point for those who want to perform cis-/trans-eQTL analysis with Matrix eQTL.

Author(s)

Andrey A Shabalin [e-mail]

References

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

See Also

See Matrix_eQTL_engine for reference and other sample code.

Examples

# Matrix eQTL by Andrey A. Shabalin
# http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL/
# 
# Be sure to use an up to date version of R and Matrix eQTL.

# source("Matrix_eQTL_R/Matrix_eQTL_engine.r");
library(MatrixEQTL)

## Location of the package with the data files.
base.dir = find.package('MatrixEQTL');

## Settings

# Linear model to use, modelANOVA, modelLINEAR, or modelLINEAR_CROSS
useModel = modelLINEAR; # modelANOVA, modelLINEAR, or modelLINEAR_CROSS

# Genotype file name
SNP_file_name = paste0(base.dir, "/data/SNP.txt");
snps_location_file_name = paste0(base.dir, "/data/snpsloc.txt");

# Gene expression file name
expression_file_name = paste0(base.dir, "/data/GE.txt");
gene_location_file_name = paste0(base.dir, "/data/geneloc.txt");

# Covariates file name
# Set to character() for no covariates
covariates_file_name = paste0(base.dir, "/data/Covariates.txt");

# Output file name
output_file_name_cis = tempfile();
output_file_name_tra = tempfile();

# Only associations significant at this level will be saved
pvOutputThreshold_cis = 2e-2;
pvOutputThreshold_tra = 1e-2;

# Error covariance matrix
# Set to numeric() for identity.
errorCovariance = numeric();
# errorCovariance = read.table("Sample_Data/errorCovariance.txt");

# Distance for local gene-SNP pairs
cisDist = 1e6;

## Load genotype data

snps = SlicedData$new();
snps$fileDelimiter = "\t";      # the TAB character
snps$fileOmitCharacters = "NA"; # denote missing values;
snps$fileSkipRows = 1;          # one row of column labels
snps$fileSkipColumns = 1;       # one column of row labels
snps$fileSliceSize = 2000;      # read file in slices of 2,000 rows
snps$LoadFile(SNP_file_name);

## Load gene expression data

gene = SlicedData$new();
gene$fileDelimiter = "\t";      # the TAB character
gene$fileOmitCharacters = "NA"; # denote missing values;
gene$fileSkipRows = 1;          # one row of column labels
gene$fileSkipColumns = 1;       # one column of row labels
gene$fileSliceSize = 2000;      # read file in slices of 2,000 rows
gene$LoadFile(expression_file_name);

## Load covariates

cvrt = SlicedData$new();
cvrt$fileDelimiter = "\t";      # the TAB character
cvrt$fileOmitCharacters = "NA"; # denote missing values;
cvrt$fileSkipRows = 1;          # one row of column labels
cvrt$fileSkipColumns = 1;       # one column of row labels
if(length(covariates_file_name)>0) {
  cvrt$LoadFile(covariates_file_name);
}

## Run the analysis
snpspos = read.table(snps_location_file_name, header = TRUE, stringsAsFactors = FALSE);
genepos = read.table(gene_location_file_name, header = TRUE, stringsAsFactors = FALSE);

me = Matrix_eQTL_main(
    snps = snps, 
    gene = gene, 
    cvrt = cvrt,
    output_file_name      = output_file_name_tra,
    pvOutputThreshold     = pvOutputThreshold_tra,
    useModel = useModel, 
    errorCovariance = errorCovariance, 
    verbose = TRUE, 
    output_file_name.cis  = output_file_name_cis,
    pvOutputThreshold.cis = pvOutputThreshold_cis,
    snpspos = snpspos, 
    genepos = genepos,
    cisDist = cisDist,
    pvalue.hist = TRUE,
    min.pv.by.genesnp = FALSE,
    noFDRsaveMemory = FALSE);

unlink(output_file_name_tra);
unlink(output_file_name_cis);

## Results:

cat('Analysis done in: ', me$time.in.sec, ' seconds', '\n');
cat('Detected local eQTLs:', '\n');
show(me$cis$eqtls)
cat('Detected distant eQTLs:', '\n');
show(me$trans$eqtls)

## Make the histogram of local and distant p-values

plot(me)