Protein and RNA pvalues for apaQTLs

Last updated: 2019-02-20

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File	Version	Author	Date	Message
Rmd	e5ff528	Briana Mittleman	2019-02-20	add distribution and pi1
html	386f80d	Briana Mittleman	2019-02-19	Build site.
Rmd	2a9d059	Briana Mittleman	2019-02-19	code for nom pvals

library(workflowr)

This is workflowr version 1.2.0
Run ?workflowr for help getting started

library(reshape2)
library(tidyverse)

── Attaching packages ──────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
✔ tidyr   0.8.1     ✔ stringr 1.4.0
✔ readr   1.1.1     ✔ forcats 0.3.0

Warning: package 'stringr' was built under R version 3.5.2

── Conflicts ─────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(qvalue)

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

Pull these in:

NucRNA=read.table("../data/ApaQTLs_otherPhen/NuclearQTLsinRNA.txt", col.names = c("SNP", "Gene", "GeneName", "RNA_P"),stringsAsFactors = F)
TotRNA=read.table("../data/ApaQTLs_otherPhen/TotalQTLsinRNA.txt", col.names = c("SNP", "Gene", "GeneName", "RNA_P"),stringsAsFactors = F)
NucProt=read.table("../data/ApaQTLs_otherPhen/NuclearQTLsinProtein.txt", col.names = c("SNP", "Gene", "GeneName", "Prot_P"),stringsAsFactors = F)
TotProt=read.table("../data/ApaQTLs_otherPhen/TotalQTLsinProtein.txt", col.names = c("SNP", "Gene", "GeneName", "Prot_P"),stringsAsFactors = F)

Pi1 values:

Nuclear:

NucRNAPi=pi0est(NucRNA$RNA_P, pi0.method = "bootstrap")
1-NucRNAPi$pi0

[1] 0.3436293

Protein

NucProtPi=pi0est(NucProt$Prot_P, pi0.method = "bootstrap")
1-NucProtPi$pi0

[1] 0.3577982

Total:

TotRNAPi=pi0est(TotRNA$RNA_P, pi0.method = "bootstrap")
1-TotRNAPi$pi0

[1] 0.3361227

Protein

TotProtPi=pi0est(TotProt$Prot_P, pi0.method = "bootstrap")
1-TotProtPi$pi0

[1] 0.3333333

Histograms:

par(mfrow=c(3,2))
hist(TotRNA$RNA_P,xlab="RNA Pvalue", main="Total apaQTLs \nin RNA")  
text(.6,50, paste("pi_1=", round((1-TotRNAPi$pi0), digit=3), sep=" "))
hist(TotProt$Prot_P,xlab="Protein Pvalue", main="Total apaQTLs \nin Protein")
text(.6,20, paste("pi_1=", round((1-TotProtPi$pi0), digit=3), sep=" "))
hist(NucRNA$RNA_P,xlab="RNA Pvalue", main="Nuclear apaQTLs \nin RNA")  
text(.6,90, paste("pi_1=", round((1-NucRNAPi$pi0), digit=3), sep=" "))
hist(NucProt$Prot_P,xlab="Protein Pvalue", main="Nuclear apaQTLs \nin Protein")
text(.6,30, paste("pi_1=", round((1-NucProtPi$pi0), digit=3), sep=" "))

Put together to look at examples and distributions:

NucOverlap=NucRNA %>% full_join(NucProt, by=c("SNP", "Gene", "GeneName"))

NucOverlap_melt=melt(NucOverlap, id.vars = c("SNP", "Gene", "GeneName"))
colnames(NucOverlap_melt)=c("SNP", "Gene", "GeneName", "Pheno", "Pvalue")

ggplot(NucOverlap_melt, aes(x=Pvalue, by=Pheno, fill=Pheno))+ geom_density(alpha=.5) +labs(title="RNA and Protien Pvalues for Nuclear apaQTLs") + scale_fill_manual(values=c("yellow","blue"))

Warning: Removed 300 rows containing non-finite values (stat_density).

TotOverlap=TotRNA %>% full_join(TotProt, by=c("SNP", "Gene", "GeneName"))

TotOverlap_melt=melt(TotOverlap, id.vars = c("SNP", "Gene", "GeneName"))
colnames(TotOverlap_melt)=c("SNP", "Gene", "GeneName", "Pheno", "Pvalue")

ggplot(TotOverlap_melt, aes(x=Pvalue, by=Pheno, fill=Pheno))+ geom_density(alpha=.5) + labs(title="RNA and Protien Pvalues for Total apaQTLs") + scale_fill_manual(values=c("yellow","blue"))

Warning: Removed 133 rows containing non-finite values (stat_density).

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     

other attached packages:
 [1] qvalue_2.12.0   forcats_0.3.0   stringr_1.4.0   dplyr_0.7.6    
 [5] purrr_0.2.5     readr_1.1.1     tidyr_0.8.1     tibble_1.4.2   
 [9] ggplot2_3.0.0   tidyverse_1.2.1 reshape2_1.4.3  workflowr_1.2.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4 splines_3.5.1    haven_1.1.2      lattice_0.20-35 
 [5] colorspace_1.3-2 htmltools_0.3.6  yaml_2.2.0       rlang_0.2.2     
 [9] pillar_1.3.0     glue_1.3.0       withr_2.1.2      modelr_0.1.2    
[13] readxl_1.1.0     bindrcpp_0.2.2   bindr_0.1.1      plyr_1.8.4      
[17] munsell_0.5.0    gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2     
[21] evaluate_0.13    labeling_0.3     knitr_1.20       broom_0.5.0     
[25] Rcpp_0.12.19     scales_1.0.0     backports_1.1.2  jsonlite_1.6    
[29] fs_1.2.6         hms_0.4.2        digest_0.6.17    stringi_1.2.4   
[33] grid_3.5.1       rprojroot_1.3-2  cli_1.0.1        tools_3.5.1     
[37] magrittr_1.5     lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2   
[41] pkgconfig_2.0.2  xml2_1.2.0       lubridate_1.7.4  assertthat_0.2.0
[45] rmarkdown_1.11   httr_1.3.1       rstudioapi_0.9.0 R6_2.3.0        
[49] nlme_3.1-137     git2r_0.24.0     compiler_3.5.1

Protein and RNA pvalues for apaQTLs

Briana Mittleman

2/19/2019