Overlap Molecular QTLs

Create reg QTL files

I want to create a python script that takes in which type of qtl and a pvalue and subsets the full file for snps that pass that filter.

subset_qtls.py

def main(inFile, outFile, qtl, cutoff):
    fout=open(outFile, "w")
    ifile=open(inFile, "r")
    cutoff=float(cutoff)
    qtl_types= ['4su_30', '4su_60', 'RNAseq', 'RNAseqGeuvadis', 'ribo', 'prot']
    if qtl not in qtl_types:
         raise NameError("QTL arg must be 4su_30, 4su_60, RNAseq, RNAseqGeuvadis, ribo, or prot") 
    elif qtl=="4su_30":
        target=4
    elif qtl=="4su_60":
        target=5
    elif qtl=="RNAseq":
        target=6
    elif qtl=="RNAseqGeuvadis":
        target=7
    elif qtl=="ribo":
        target =8
    elif qtl=="prot":
        target=9
    for num,ln in enumerate(ifile):
        if num > 0 :
            line_list = ln.split()
            chrom=line_list[0][3:]
            pos=line_list[1]
            rsid=line_list[2]
            geneID=line_list[3]
            val = line_list[target].split(":")[0]
            if val == "NA":
              continue
            else:
                val = float(val)
                if val <= cutoff:
                    fout.write("%s:%s\t%s\t%s\t%f\n"%(chrom, pos, rsid, geneID,val))
    


if __name__ == "__main__":
    import sys

    qtl = sys.argv[1]
    cutoff= sys.argv[2]
    
    inFile = "/project2/gilad/briana/threeprimeseq/data/otherQTL/summary_betas_ste_100kb.txt"
    outFile = "/project2/gilad/briana/threeprimeseq/data/otherQTL/summary_betas_ste_100kb.%s%s.txt"%(qtl, cutoff)
    main(inFile, outFile, qtl, cutoff)

I can run this to subset by each qtl at .05

run_subsetQTLs05.sh

#!/bin/bash

#SBATCH --job-name=run_subsetqtl05
#SBATCH --account=pi-gilad
#SBATCH --time=24:00:00
#SBATCH --output=run_subsetqtl05.out
#SBATCH --error=run_subsetqtl05.err
#SBATCH --partition=gilad
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

#qtls=('4su_30', '4su_60', 'RNAseq', 'RNAseqGeuvadis', 'ribo', 'prot')  

for i in 4su_30 4su_60 RNAseq RNAseqGeuvadis ribo prot; do
    python subset_qtls.py $i .05 
done

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 1.3.1
✔ readr   1.1.1     ✔ forcats 0.3.0

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

library(workflowr)

This is workflowr version 1.1.1
Run ?workflowr for help getting started

library(reshape2)

Attaching package: 'reshape2'

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

    smiths

library(readr)

nuc.nom=read.table("../data/nom_QTL_opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes_onetenth.txt", header = F, stringsAsFactors = F)
colnames(nuc.nom)= c("peakID", "snpID", "dist", "nuc_pval", "slope")

QTL_names=c("snpID", "snpid2","Gene", "pval")

fourSU30= read.table("../data/other_qtls/summary_betas_ste_100kb.4su_30.05.txt", header=F, stringsAsFactors = F, col.names = QTL_names)

fourSU60=read.table("../data/other_qtls/summary_betas_ste_100kb.4su_60.05.txt", header=F, stringsAsFactors = F, col.names = QTL_names)

RNAseq=read.table("../data/other_qtls/summary_betas_ste_100kb.RNAseq.05.txt", header=F, stringsAsFactors = F, col.names = QTL_names)

guevardis=read.table("../data/other_qtls/summary_betas_ste_100kb.RNAseqGeuvadis.05.txt", header=F, stringsAsFactors = F, col.names = QTL_names)

ribo=read.table("../data/other_qtls/summary_betas_ste_100kb.ribo.05.txt", header=F, stringsAsFactors = F, col.names = QTL_names)

prot=read.table("../data/other_qtls/summary_betas_ste_100kb.prot.05.txt", header=F, stringsAsFactors = F, col.names = QTL_names)

Filter nuc by other QTLs

Overlap the files:

fourSU30AndNuc= fourSU30 %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, nuc_pval)
fourSU30_unif=runif(nrow(fourSU30AndNuc))

fourSU60AndNuc= fourSU60 %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, nuc_pval)
fourSU60_unif=runif(nrow(fourSU60AndNuc))


RNAAndNuc= RNAseq %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, nuc_pval)
RNAseq_unif=runif(nrow(RNAAndNuc))


GuevAndNuc= guevardis %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, nuc_pval)
guev_unif=runif(nrow(GuevAndNuc))


riboAndNuc= ribo %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, nuc_pval)
ribo_unif=runif(nrow(riboAndNuc))

protAndNuc= prot %>% inner_join(nuc.nom, by="snpID") %>% select(snpID, nuc_pval)
prot_unif=runif(nrow(protAndNuc))

Plot overlapping QTLs

Plot results:

qqplot(-log10(runif(nrow(nuc.nom))), -log10(nuc.nom$nuc_pval),ylab="-log10 Nuclear nominal pvalue", xlab="Uniform expectation", main="Nuclear Nominal pvalues for all snps")
points(sort(-log10(fourSU30_unif)), sort(-log10(fourSU30AndNuc$nuc_pval)), col="Red")
points(sort(-log10(fourSU60_unif)), sort(-log10(fourSU60AndNuc$nuc_pval)), col="Orange")
points(sort(-log10(RNAseq_unif)), sort(-log10(RNAAndNuc$nuc_pval)), col="Yellow")
points(sort(-log10(guev_unif)), sort(-log10(GuevAndNuc$nuc_pval)), col="Green")
points(sort(-log10(ribo_unif)), sort(-log10(riboAndNuc$nuc_pval)), col="Blue")
points(sort(-log10(prot_unif)), sort(-log10(protAndNuc$nuc_pval)), col="Purple")
abline(0,1)


legend("topleft", legend=c("All SNPs", "4su 30", "4su 60", "RNAseq", "Guevadis RNA", "Ribo", "Col"), col=c("black", "red", "orange", "yellow", "green", "blue", "purple"), pch=19)

Session information

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

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] reshape2_1.4.3  workflowr_1.1.1 forcats_0.3.0   stringr_1.3.1  
 [5] dplyr_0.7.6     purrr_0.2.5     readr_1.1.1     tidyr_0.8.1    
 [9] tibble_1.4.2    ggplot2_3.0.0   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4  haven_1.1.2       lattice_0.20-35  
 [4] colorspace_1.3-2  htmltools_0.3.6   yaml_2.2.0       
 [7] rlang_0.2.2       R.oo_1.22.0       pillar_1.3.0     
[10] glue_1.3.0        withr_2.1.2       R.utils_2.7.0    
[13] modelr_0.1.2      readxl_1.1.0      bindrcpp_0.2.2   
[16] bindr_0.1.1       plyr_1.8.4        munsell_0.5.0    
[19] gtable_0.2.0      cellranger_1.1.0  rvest_0.3.2      
[22] R.methodsS3_1.7.1 evaluate_0.11     knitr_1.20       
[25] broom_0.5.0       Rcpp_0.12.18      scales_1.0.0     
[28] backports_1.1.2   jsonlite_1.5      hms_0.4.2        
[31] digest_0.6.16     stringi_1.2.4     grid_3.5.1       
[34] rprojroot_1.3-2   cli_1.0.0         tools_3.5.1      
[37] magrittr_1.5      lazyeval_0.2.1    crayon_1.3.4     
[40] whisker_0.3-2     pkgconfig_2.0.2   xml2_1.2.0       
[43] lubridate_1.7.4   assertthat_0.2.0  rmarkdown_1.10   
[46] httr_1.3.1        rstudioapi_0.7    R6_2.2.2         
[49] nlme_3.1-137      git2r_0.23.0      compiler_3.5.1