Last updated: 2019-02-19

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File Version Author Date Message
Rmd 2a9d059 Briana Mittleman 2019-02-19 code for nom pvals

For a lot of this project I have been looking at the relationship between APA, RNA, and protein. I want to use trhis analysis to get the nominal pvalues for the associations of the snp:gene pairs found in the APA qtl analysis. This will help me find examples and look at the distributions overall.

I want a file that has the nominal pvalues for each of the apaQTls in the total 3’, nuclear 3’, RNA, and protein. I will have to convert the gene names.

Start with a dictionary of the QTLs. It will have the snp as the key and converted gene as the value. I can then write out the associations.

I can do this seperate for RNA and protein with total and nuclear by having a script that can take all of the combinations. After I get the results I can merge them and add NAs for missing measurements.

I can ask questions like, given a snp is a apaQTL what is nom association in other pheno.

Molpval4ApaQTL.py



def main(QTL, phen, outF, phenotype):  
    fout=open(outF,"w")
    geneNames=open("/project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt","r")
    #gene name dictionary  
    geneDic={}
    geneDicOpp={}
    for i, ln in enumerate(geneNames):
        if i >0:
            ID=ln.split()[0]
            gene=ln.split()[1]
            if gene not in geneDic.keys():
                geneDic[gene]=[ID]
            else: 
                geneDic[gene].append(ID)
            geneDicOpp[ID]=gene
    print(geneDicOpp.keys())
    #qtl dic
    qtlDic={}
    for ln in open(QTL,"r"):
        snp=ln.split()[5]
        gene=ln.split()[0].split(":")[-1].split("_")[0]
        #gene_id=geneDic[gene]
        qtlDic[snp]=gene
    #loop over pheno
    for ln in open(phen,"r"):
        snp=ln.split()[1]
        if snp in qtlDic.keys():
            if phenotype == "RNA":
                gene=ln.split()[0].split(".")[0]
                if gene not in geneDicOpp.keys():
                    next
                geneName=geneDicOpp[gene]
            else:
                gene=ln.split()[0]
                if gene not in geneDicOpp.keys():
                    next
                geneName=geneDicOpp[gene]
            if qtlDic[snp]==geneName:
               pval=ln.split()[3]
               fout.write("%s\t%s\t%s\t%s\n"%(snp, gene, geneName, pval))
    fout.close()





    
if __name__ == "__main__":
    import sys
    fraction = sys.argv[1]
    pheno = sys.argv[2]
    inQTL="/project2/gilad/briana/threeprimeseq/data/ApaQTLs/%sapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt"%(fraction)
    if pheno == "RNA":
        inPhen="/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_RNAseq_phase2.fixed.nominal.out"
    if pheno =="Protein":  
        inPhen= "/project2/gilad/briana/threeprimeseq/data/molecular_QTLs/nom/fastqtl_qqnorm_prot_fixed.nominal.out"
    out="/project2/gilad/briana/threeprimeseq/data/ApaQTLs_otherPhen/%sQTLsin%s.txt"%(fraction, pheno)
    main(inQTL, inPhen, out, pheno)

Run this on all combinations:

run_Molpval4ApaQTL.sh

#!/bin/bash

#SBATCH --job-name=run_Molpval4ApaQTL
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=run_Molpval4ApaQTL.out
#SBATCH --error=run_Molpval4ApaQTL.err
#SBATCH --partition=broadwl
#SBATCH --mem=30G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env

python Molpval4ApaQTL.py Total RNA
python Molpval4ApaQTL.py Nuclear RNA
python Molpval4ApaQTL.py Total Protein
python Molpval4ApaQTL.py Nuclear Protein



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_0.12.19    digest_0.6.17   rprojroot_1.3-2
 [5] backports_1.1.2 git2r_0.24.0    magrittr_1.5    evaluate_0.13  
 [9] stringi_1.2.4   fs_1.2.6        whisker_0.3-2   rmarkdown_1.11 
[13] tools_3.5.1     stringr_1.4.0   glue_1.3.0      yaml_2.2.0     
[17] compiler_3.5.1  htmltools_0.3.6 knitr_1.20