eQTL
Jeff Leek
Dependencies
This document depends on the following packages:
library(devtools)
library(Biobase)
library(MatrixEQTL)To install these packages you can use the code (or if you are compiling the document, remove the eval=FALSE from the chunk.)
install.packages(c("devtools","MatrixEQTL"))
source("http://www.bioconductor.org/biocLite.R")
biocLite(c("Biobase"))Download the data
Here we are going to follow along with the tutorial on MatrixEQTL. First we find the files
base.dir = find.package("MatrixEQTL")
SNP_file_name = paste(base.dir, "/data/SNP.txt", sep="");
expression_file_name = paste(base.dir, "/data/GE.txt", sep="")
covariates_file_name = paste(base.dir, "/data/Covariates.txt", sep="")
output_file_name = tempfile()Next we load the data so we can see it
expr = read.table(expression_file_name,sep="\t",
header=T,row.names=1)
expr[1,]## Sam_01 Sam_02 Sam_03 Sam_04 Sam_05 Sam_06 Sam_07 Sam_08 Sam_09
## Gene_01 4.91 4.63 5.18 5.07 5.74 5.09 5.31 5.29 4.73
## Sam_10 Sam_11 Sam_12 Sam_13 Sam_14 Sam_15 Sam_16
## Gene_01 5.72 4.75 4.54 5.01 5.03 4.84 4.44snps = read.table(SNP_file_name,sep="\t",
header=T,row.names=1)
snps[1,]## Sam_01 Sam_02 Sam_03 Sam_04 Sam_05 Sam_06 Sam_07 Sam_08 Sam_09
## Snp_01 2 0 2 0 2 1 2 1 1
## Sam_10 Sam_11 Sam_12 Sam_13 Sam_14 Sam_15 Sam_16
## Snp_01 1 2 2 1 2 2 1cvrt = read.table(covariates_file_name,sep="\t",
header=T,row.names=1)eQTL is linear regession
The simplest eQTL analysis just computes linear regression models for each SNP/gene pair.
e1 = as.numeric(expr[1,])
s1 = as.numeric(snps[1,])
lm1 = lm(e1 ~ s1)
tidy(lm1)## term estimate std.error statistic p.value
## 1 (Intercept) 4.92967742 0.2137508 23.0627348 1.546033e-12
## 2 s1 0.06387097 0.1386998 0.4604979 6.522304e-01We can visualize the data and the model fits
plot(e1 ~ jitter(s1),
col=(s1+1),xaxt="n",xlab="Genotype",ylab="Expression")
axis(1,at=c(0:2),labels=c("AA","Aa","aa"))
lines(lm1$fitted ~ s1,type="b",pch=15,col="darkgrey")
Fitting many eQTL models with MatrixEQTL
Set general parameters
We need to set up the p-value cutoff and the error model (in this case assuming independent errors)
pvOutputThreshold = 1e-2
errorCovariance = numeric()
useModel = modelLINEARSet the data up
Now we need to set up the snp and gene expression data in the special format required by the MatrixEQTL package
snps = SlicedData$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$fileSliceSize = 2000 # read file in pieces of 2,000 rows
snps$LoadFile( SNP_file_name )## Rows read: 15 done.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 pieces of 2,000 rows
gene$LoadFile(expression_file_name)## Rows read: 10 done.cvrt = SlicedData$new()Running MatrixEQTL
We can now run the code to calculate the eQTL that we are interested in
me = Matrix_eQTL_engine(
snps = snps,
gene = gene,
cvrt = cvrt,
output_file_name = NULL,
pvOutputThreshold = pvOutputThreshold,
useModel = useModel,
errorCovariance = errorCovariance,
verbose = TRUE,
pvalue.hist = TRUE,
min.pv.by.genesnp = FALSE,
noFDRsaveMemory = FALSE);## Processing covariates
## Task finished in 0.001 seconds
## Processing gene expression data (imputation, residualization, etc.)
## Task finished in 0.001 seconds
## Creating output file(s)
## Task finished in 0.006 seconds
## Performing eQTL analysis
## 100.00% done, 1 eQTLs
## Task finished in 0.01 seconds
## Understanding the results
We can make a plot of all the p-values from the tests
plot(me)
We can also figure look at the number and type of eQTL
me$all$neqtls## [1] 1me$all$eqtls## snps gene statistic pvalue FDR beta
## 1 Snp_11 Gene_06 -3.007106 0.00941791 1 -0.2916667More information
eQTL is an entire field of research.
Session information
Here is the session information
devtools::session_info()## setting value
## version R version 3.2.1 (2015-06-18)
## system x86_64, darwin10.8.0
## ui RStudio (0.99.447)
## language (EN)
## collate en_US.UTF-8
## tz America/New_York
##
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