Last updated: 2019-04-18

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Knit directory: apaQTL/analysis/

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Rmd 017f5c0 brimittleman 2019-04-18 add map apa qtl pipeline

In this analysis I will call apaQTls in both fractions. I will start with the phenotype files and normalized the counts using the leafcutter package in order to run the fastq QTL mapper.

Prepare phenotypes for QTL- phenotype dir

It is best to run this analysis in the data/phenotype directory. I have copied the leafcutter prepare_phenotype_table.py to the code directroy to use here.

#!/bin/bash
module load python

gzip APApeak_Phenotype_GeneLocAnno.Total.fc
gzip APApeak_Phenotype_GeneLocAnno.Nuclear.fc

python ../../code/prepare_phenotype_table.py/APApeak_Phenotype_GeneLocAnno.Total.fc
python ../../code/prepare_phenotype_table.py/APApeak_Phenotype_GeneLocAnno.Nuclear.fc

This will output bash scripts to run.

module load Anaconda3
source activate three-prime-env

sh APApeak_Phenotype_GeneLocAnno.Nuclear.fc.gz_prepare.sh
sh APApeak_Phenotype_GeneLocAnno.Nuclear.fc.gz_prepare.sh

Subset the PCs to use the first 2 in the qtl calling:

module load Anaconda3
source activate three-prime-env

head -n 3 APApeak_Phenotype_GeneLocAnno.Nuclear.fc.gz.PCs > APApeak_Phenotype_GeneLocAnno.Nuclear.fc.gz.2PCs
head -n 3 APApeak_Phenotype_GeneLocAnno.Total.fc.gz.PCs > APApeak_Phenotype_GeneLocAnno.Total.fc.gz.2PCs

Call QTLs- code dir

Next I will need to make a sample list. From the code directory:

python makeSampleList.py

Prepare directroy

mkdir ../data/apaQTLNominal
mkdir ../data/apaQTLPermuted

Run the code to call QTLs within 1mb of each PAS peak. I run both a nominal pass and a permuted pas. The permulted pas chosses the best snp for each peak gene pair.

sbatch apaQTL_Nominal.sh
sbatch apaQTL_permuted.sh

Concatinate all of the results in the permuted set. I do this so I can account for multiple testing with the benjamini hochberg test.

Concatinate

Rscripts apaQTLCorrectPvalMakeQQ.R  


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] workflowr_1.2.0 Rcpp_1.0.0      digest_0.6.18   rprojroot_1.3-2
 [5] backports_1.1.3 git2r_0.24.0    magrittr_1.5    evaluate_0.13  
 [9] stringi_1.3.1   fs_1.2.6        whisker_0.3-2   rmarkdown_1.11 
[13] tools_3.5.1     stringr_1.4.0   glue_1.3.0      xfun_0.5       
[17] yaml_2.2.0      compiler_3.5.1  htmltools_0.3.6 knitr_1.21