Last updated: 2019-03-11

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File Version Author Date Message
Rmd 88e8b7d Briana Mittleman 2019-03-11 try with permuted values

Look for the snps that are QTLs in the nuclear fraction but not the total fraction. To do this I want to plot the association in the total fraction vs. the association in the nuclear fraction. I can look for snps that fall off the 1:1 line towards the nuclear fraction.

I need to look for the snps tested in both fractions. The nominal results are in:

  • /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt
  • /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt

This will not include the 18982867 more assocaitions in nucelar. I can only look at those that are tested in both analyses.

Format of file: * peakID * snp * dist * pval * slope (effect size)

I can make a dictionary with gene:snp:peak as the keys and a dictionary for the values- the inner dictionary will have the fraction as the key and the pvalue as the value

totalandnuclear_commonassociation.py

totRes=open("/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt","r")
nucRes=open("/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt", "r")  


outfile=open("/project2/gilad/briana/threeprimeseq/data/NucSpecQTL/TotNucRes_overlapassociations.txt", "w")

resDict={}
for ln in totRes:
    gene=ln.split()[0].split(":")[-1].split("_")[0]
    peak=ln.split()[0].split(":")[-1].split("_")[-1]
    snp=ln.split()[1]
    id=gene + ":" + peak + ":" + snp
    pval=ln.split()[3]
    resDict[id]={}
    resDict[id]["Total"]=pval

for ln in nucRes:
    gene=ln.split()[0].split(":")[-1].split("_")[0]
    peak=ln.split()[0].split(":")[-1].split("_")[-1]
    snp=ln.split()[1]
    id=gene + ":" + peak + ":" + snp
    pval=ln.split()[3]
    if id in resDict.keys():
        resDict[id]["Nuclear"]=pval
    else:
        continue   
        
#now i have a double dictionary. i need to write it out. i want id, the total pval, then the nuc pval  
for outer, inner in resDict.items():
    id=outer
    outPval=[]
    for key in inner:
        outPval.append(inner[key])
    print(outPval)
    if(len(outPval))==2:
         outfile.write("%s\t%s\t%s\n"%(id, outPval[0], outPval[1]))


outfile.close()

run it: run_totalandnuclear_commonassociation.sh

#!/bin/bash

#SBATCH --job-name=run_totalandnuclear_commonassociation
#SBATCH --account=pi-yangili1
#SBATCH --time=5:00:00
#SBATCH --output=run_totalandnuclear_commonassociation.out
#SBATCH --error=run_totalandnuclear_commonassociation.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env


python totalandnuclear_commonassociation.py

Tot assoc: 105456802 nuclear assoc: 124439669 common: 94176434 This means 41 percent are common. This means in the make phenotpye step, different genes and peaks are removed. Results: /project2/gilad/briana/threeprimeseq/data/NucSpecQTL/TotNucRes_overlapassociations.txt

makeTotalNucAssocPlot.R

library(tidyverse)
library(data.table)

file=fread("../data/NucSpecQTL/TotNucRes_overlapassociations.txt", col.names =c("ID","Total", "Nuclear"))

cor_plot=ggplot(file, aes(x=-log10(Total), y=-log10(Nuclear))) + geom_point() + geom_density2d(na.rm = TRUE, size = 1, colour = 'red') + labs(title="Common Peak/Snp/ID associations Total and Nuclear", x="-log10(Total Pval)", y="-log10(Nuclear Pval)") + geom_smooth(aes(x=-log10(Total),y=-log10(Nuclear)),method = "lm")


#print(summary(lm(data=file, -log10(Total) ~ -log10(Nuclear))))

#cor_plot

#ggsave(plot, "/project2/gilad/briana/threeprimeseq/output/TotalvNucPavalCommonAssoc.png")

in python with pyplotlib:


import matplotlib.pyplot as plt
import numpy as np


inF=open("/project2/gilad/briana/threeprimeseq/data/NucSpecQTL/TotNucRes_overlapassociations.txt", "r")


total=[]
nuclear=[]
for ln in inF:
  loca, tot, nuc = ln.split()
  total.append(float(tot))
  nuclear.append(float(nuc))

  
np.asarray(total)
np.asarray(nuclear)
diff =np.subtract(total, nuclear)

plt.plot(total,nuclear)
plt.savefig("/project2/gilad/briana/threeprimeseq/output/TotalvNucPavalCommonAssoc.png")

run_makeTotalNucAssocPlot.sh

#!/bin/bash

#SBATCH --job-name=run_makeTotalNucAssocPlot
#SBATCH --account=pi-yangili1
#SBATCH --time=5:00:00
#SBATCH --output=run_makeTotalNucAssocPlot.out
#SBATCH --error=run_makeTotalNucAssocPlot.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env

Rscript makeTotalNucAssocPlot.R

These are too many values: I need to use the permuted. this will jsut be the pvalue for the same peak:gene combo

try with perm

totalandnuclear_commonassociationPerm.py

totRes=open("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_permRes.txt","r")
nucRes=open("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_permRes.txt", "r")  


outfile=open("/project2/gilad/briana/threeprimeseq/data/NucSpecQTL/TotNucRes_Permoverlapassociations.txt", "w")

resDict={}
for ln in totRes:
    gene=ln.split()[0].split(":")[-1].split("_")[0]
    peak=ln.split()[0].split(":")[-1].split("_")[-1]
    id=gene + ":" + peak 
    pval=ln.split()[-2]
    resDict[id]={}
    resDict[id]["Total"]=pval

for ln in nucRes:
    gene=ln.split()[0].split(":")[-1].split("_")[0]
    peak=ln.split()[0].split(":")[-1].split("_")[-1]
    id=gene + ":" + peak
    pval=ln.split()[-2]
    if id in resDict.keys():
        resDict[id]["Nuclear"]=pval
    else:
        continue   
        
#now i have a double dictionary. i need to write it out. i want id, the total pval, then the nuc pval  
for outer, inner in resDict.items():
    id=outer
    outPval=[]
    for key in inner:
        outPval.append(inner[key])
    print(outPval)
    if(len(outPval))==2:
         outfile.write("%s\t%s\t%s\n"%(id, outPval[0], outPval[1]))


outfile.close()        
        

library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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library(data.table)
Warning: package 'data.table' was built under R version 3.5.2

Attaching package: 'data.table'
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    between, first, last
The following object is masked from 'package:purrr':

    transpose
file=fread("../data/NucSpecQTL/TotNucRes_Permoverlapassociations.txt", col.names =c("ID","Total", "Nuclear"))

cor_plot=ggplot(file, aes(x=Total, y=Nuclear)) + geom_point() + geom_density2d(na.rm = TRUE, size = 1, colour = 'red') + labs(title="Common Peak associations Total and Nuclear") 

cor_plot
Warning: Removed 20 rows containing missing values (geom_point).

This is similar to what was happening in the nominal case.

I think I need to plot just the total in nuclaer and nuclear in total. this way i can see the outliers. 

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] data.table_1.12.0 forcats_0.4.0     stringr_1.4.0    
 [4] dplyr_0.8.0.1     purrr_0.3.1       readr_1.3.1      
 [7] tidyr_0.8.3       tibble_2.0.1      ggplot2_3.1.0    
[10] tidyverse_1.2.1  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 plyr_1.8.4       pillar_1.3.1    
 [5] compiler_3.5.1   git2r_0.24.0     workflowr_1.2.0  tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.13   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.3.1      cli_1.0.1        rstudioapi_0.9.0 yaml_2.2.0      
[21] haven_2.1.0      xfun_0.5         withr_2.1.2      xml2_1.2.0      
[25] httr_1.4.0       knitr_1.21       hms_0.4.2        generics_0.0.2  
[29] fs_1.2.6         rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5
[33] glue_1.3.0       R6_2.4.0         readxl_1.3.0     rmarkdown_1.11  
[37] modelr_0.1.4     magrittr_1.5     whisker_0.3-2    MASS_7.3-51.1   
[41] backports_1.1.3  scales_1.0.0     htmltools_0.3.6  rvest_0.3.2     
[45] assertthat_0.2.0 colorspace_1.4-0 labeling_0.3     stringi_1.3.1   
[49] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4