###Installing meQTLfunc can be installed by using the devtools package.
install_github('shilab/meQTL_functions')
library(meQTLfunc)
me<-mxeqtl('CNV_matrix.out.filter','CNV_position','liver_expression.out.filter','gene_position','liver_cis_results',0.05)
## Rows read: 2,000
## Rows read: 2529 done.
## SNPs before filtering: 2529
## Rows read: 2,000
## Rows read: 4,000
## Rows read: 4557 done.
## Matching data files and location files
## 2591 of 4557 genes matched
## 2529 of 2529 SNPs matched
## Task finished in 0.353 seconds
## Reordering SNPs
##
## Task finished in 0.094 seconds
## Reordering genes
##
## Task finished in 0.098 seconds
## Processing covariates
## Task finished in 0.003 seconds
## Processing gene expression data (imputation, residualization, etc.)
## Task finished in 0.017 seconds
## Creating output file(s)
## Task finished in 0.009 seconds
## Performing eQTL analysis
## 16.66% done, 585 cis-eQTLs
## 66.66% done, 605 cis-eQTLs
## 83.33% done, 780 cis-eQTLs
## Task finished in 0.91 seconds
##
## Analysis done in: 1.457 seconds
## Detected 780 local eQTLs:
## Detected distant eQTLs:
expr<-read.table('./liver_expression.out.filter', header = TRUE, stringsAsFactors = FALSE,na.string="NA")
genot<-read.table('./CNV_matrix.out.filter', header = TRUE, stringsAsFactors = FALSE,na.string="NA")
CorrBoxPlot(me,.2,expr,genot,visual=T,pdf_file="res.pdf")
## [1] 0.7859 0.7799 0.7799 -0.7799 0.7554 -0.7412 -0.7301 0.7113
## [9] 0.7015 0.6977 -0.6936 0.6935 0.6902 -0.6845 0.6840 -0.6839
## [17] 0.6828 0.6782 0.6773 0.6745 0.6744 0.6744 -0.6710 0.6701
## [25] 0.6693 0.6685 0.6658 0.6634 0.6608 -0.6540 0.6524 -0.6524
## [33] 0.6524 -0.6524 0.6480 -0.6463 0.6422 0.6403 0.6393 0.6383
## [41] -0.6345 0.6345 -0.6345 -0.6345 -0.6336 0.6333 0.6333 -0.6333
## [49] 0.6328 0.6306 0.6289 -0.6258 0.6256 -0.6256 -0.6256 0.6246
## [57] -0.6228 0.6226 0.6221 0.6196 0.6188 0.6183 0.6183 -0.6183
## [65] 0.6155 0.6148 0.6143 -0.6118 -0.6099 -0.6095 0.6093 0.6083
## [73] -0.6078 0.6075 0.6065 0.6063