Protein and RNA pvalues for apaQTLs

apaQTLs in RNA and prot

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(cowplot)

Attaching package: 'cowplot'

The following object is masked from 'package:ggplot2':

    ggsave

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:

• RNA

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:

• RNA

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:

png("../output/plots/OverlapRNAandProtPi.png")
par(mfrow=c(2,2))
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=" "))
dev.off()

quartz_off_screen 
                2 

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).

Past versions of unnamed-chunk-10-1.png

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).

Past versions of unnamed-chunk-11-1.png

Look at the pi1 with bootstrapping:

set.seed(1)
pi1Bootstrap= function(pval){
    output=integer(100)
    for (i in 1:length(output)){
      samp=sample(pval,length(pval), replace = T)
      Spi0=pi0est(samp, pi0.method = "bootstrap")
      pi1=1-Spi0$pi0
      output[i]=pi1
    }
    return(output)
}
#TotP_sim=pi1Bootstrap(TotProt$Prot_P)
TotR_sim=pi1Bootstrap(TotRNA$RNA_P)
NucP_sim=pi1Bootstrap(NucProt$Prot_P)
NucR_sim=pi1Bootstrap(NucRNA$RNA_P)

TotP_est=1-TotProtPi$pi0
TotR_est=1-TotRNAPi$pi0
NucP_est=1-NucProtPi$pi0
NucR_est=1-NucRNAPi$pi0

Fraction=c("Total", "Total", "Nuclear", "Nuclear")
Category=c("Protein", "RNA","Protein", "RNA")
Estimate=c(TotP_est,TotR_est,NucP_est,NucR_est)
SD=c(0, sd(TotR_sim),sd(NucP_sim), sd(NucR_sim))

Pi1DF=as.data.frame(cbind(Fraction, Category,Estimate,SD))
Pi1DF$Estimate= as.numeric(as.character(Pi1DF$Estimate))
Pi1DF$SD= as.numeric(as.character(Pi1DF$SD))

ggplot(Pi1DF,aes(x=Category, y=Estimate, col=Category)) + geom_point() + geom_errorbar(aes(ymin=(Estimate-SD), ymax=(Estimate+SD)),width=.1) + facet_grid(~Fraction) + scale_color_manual(values=c("red", "blue")) + labs(title="Pi1 Sharing Between apaQTLs and other molQTLs")

Past versions of unnamed-chunk-14-1.png

Split explained and unexplained

I want to look at genes for unexplained eQTLs. I need to make a list of the genes with an unexplained eQTL.

getUnexpeQTLGenes.py

inFile="/project2/gilad/briana/threeprimeseq/data/eQTL_Lietal/unexplained_FDR10.txt"
outFile=open("/project2/gilad/briana/threeprimeseq/data/eQTL_Lietal/unexplained_FDR10_genes.txt", "w")
for ln in open(inFile, "r"):
    if length(ln.split())==3:
        gene=ln.split()[2]
        outFile.write("%s\n"%(gene))
    else: 
       genes=ln.split()[2:]
       for i in genes:
           outFile.write("%s\n"%i)
           
outFile.close()

geneNames=read.table("../data/ensemble_to_genename.txt",stringsAsFactors = F, header = T, sep="\t")
unexpGene=read.table("../data/eQTLs_Lietal/unexplained_FDR10_genes.txt", header = F, col.names = c("Gene.stable.ID"),stringsAsFactors = F) %>% inner_join(geneNames, by="Gene.stable.ID")

colnames(unexpGene)=c("Gene.stable.ID", "GeneName", "Source")

Now I want to seperate the eQTL pvalue results by these gene.

Total:

TotRNAun=TotRNA %>% semi_join(unexpGene,by="GeneName")
TotRNAUnPval=TotRNAun %>% select(RNA_P) %>% mutate(Category="Unexplained")
TotRNAEx=TotRNA %>% anti_join(unexpGene, by="GeneName")
TotRNAExPval=TotRNAEx%>% select(RNA_P) %>% mutate(Category="Explained")

#full data frame  
AllRNAP_tot=as.data.frame(rbind(TotRNAUnPval,TotRNAExPval))

totExvUn=ggplot(AllRNAP_tot, aes(by=Category, x=RNA_P, fill=Category))+ geom_density(alpha=.3) + scale_fill_manual(values=c("red", "blue")) + labs(x="eQTL association pvalue", title="Total apaQTL associations \n in eQTLs analysis") + annotate("text", label="P < .0001", x=.75, y=2) + annotate("text", label="***", x=.75, y=1.8)

Nuclear:

NucRNAun=NucRNA %>% semi_join(unexpGene,by="GeneName")
NucRNAUnPval=NucRNAun %>% select(RNA_P) %>% mutate(Category="Unexplained")
NucRNAEx=NucRNA %>% anti_join(unexpGene, by="GeneName")
NucRNAExPval=NucRNAEx%>% select(RNA_P) %>% mutate(Category="Explained")

AllRNAP_nuc=as.data.frame(rbind(NucRNAUnPval,NucRNAExPval))

nucExvUn=ggplot(AllRNAP_nuc, aes(by=Category, x=RNA_P, fill=Category))+ geom_density(alpha=.3) + scale_fill_manual(values=c("red", "blue")) + labs(x="eQTL association pvalue", title="Nuclear apaQTL associations \n in eQTLs analysis") +annotate("text", label="P < .0001", x=.75, y=1.5)+ annotate("text", label="***", x=.75, y=1.4)

bothExOver=plot_grid(totExvUn,nucExvUn)


bothExOver

Past versions of unnamed-chunk-19-1.png

ggsave(bothExOver, file="../output/plots/apaQTLsinExplainedvUnexplainedeQTLs.png", height = 5, width = 10)

Try to get pi1 for these analysis:

#TotRNAunpi=pi0est(TotRNAun$RNA_P, pi0.method = "bootstrap",na.rm=T)
TotRNAExpi=pi0est(TotRNAEx$RNA_P, pi0.method = "bootstrap")
1- TotRNAExpi$pi0

[1] 0.2875817

NucRNAunpi=pi0est(NucRNAun$RNA_P, pi0.method = "bootstrap")
1-NucRNAunpi$pi0

[1] 0.6111111

NucRNAExpi=pi0est(NucRNAEx$RNA_P, pi0.method = "bootstrap")
1- TotRNAExpi$pi0

[1] 0.2875817

Look at significance in a hypergeometric

I will put the number that are less than .1 and those that are more in each set. I will make a 2 by 2 table with exp/unexplained as cols and rows as sig vs not.

• Total

alpha=.05

TotRNAUnPval_sig=TotRNAUnPval %>% filter(RNA_P <=.05) %>% nrow()
TotRNAUnPval_Nsig=TotRNAUnPval %>% filter(RNA_P >.05) %>% nrow()

TotRNAExPval_sig=TotRNAExPval %>% filter(RNA_P <=.05) %>% nrow()
TotRNAExPval_Nsig=TotRNAExPval %>% filter(RNA_P >.05) %>% nrow()

TotalSig=c(TotRNAUnPval_sig,TotRNAExPval_sig)
TotNSig=c(TotRNAUnPval_Nsig,TotRNAExPval_Nsig)

TotalMatrix=rbind(TotalSig,TotNSig )
TotalMatrix

         [,1] [,2]
TotalSig   18   45
TotNSig    17  159

Lower tail is false because we are asknig if we get more sig than expected

phyper(18,35,204,63, lower.tail = F)

[1] 0.000126086

• Nuclear

NucRNAUnPval_sig=NucRNAUnPval %>% filter(RNA_P <=.1) %>% nrow()
NucRNAUnPval_Nsig=NucRNAUnPval %>% filter(RNA_P >.1) %>% nrow()

NucRNAExPval_sig=NucRNAExPval %>% filter(RNA_P <=.1) %>% nrow()
NucRNAExPval_Nsig=NucRNAExPval %>% filter(RNA_P >.1) %>% nrow()

NucSig=c(NucRNAUnPval_sig,NucRNAExPval_sig)
NucNSig=c(NucRNAUnPval_Nsig,NucRNAExPval_Nsig)

NucMatrix=rbind(NucSig,NucNSig )
NucMatrix

        [,1] [,2]
NucSig    31  110
NucNSig   29  348

phyper(31,69,458,141, lower.tail = F)

[1] 0.0001318397

Session information

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] bindrcpp_0.2.2  qvalue_2.12.0   cowplot_0.9.3   forcats_0.3.0  
 [5] stringr_1.4.0   dplyr_0.7.6     purrr_0.2.5     readr_1.1.1    
 [9] tidyr_0.8.1     tibble_1.4.2    ggplot2_3.0.0   tidyverse_1.2.1
[13] 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     bindr_0.1.1      plyr_1.8.4       munsell_0.5.0   
[17] gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2      evaluate_0.13   
[21] labeling_0.3     knitr_1.20       broom_0.5.0      Rcpp_0.12.19    
[25] scales_1.0.0     backports_1.1.2  jsonlite_1.6     fs_1.2.6        
[29] hms_0.4.2        digest_0.6.17    stringi_1.2.4    grid_3.5.1      
[33] rprojroot_1.3-2  cli_1.0.1        tools_3.5.1      magrittr_1.5    
[37] lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2 
[41] xml2_1.2.0       lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.11  
[45] httr_1.3.1       rstudioapi_0.9.0 R6_2.3.0         nlme_3.1-137    
[49] git2r_0.24.0     compiler_3.5.1