Last updated: 2019-03-12
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File | Version | Author | Date | Message |
---|---|---|---|---|
html | 0537a72 | Briana Mittleman | 2019-03-11 | Build site. |
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:
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
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)
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Attaching package: 'data.table'
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library(cowplot)
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Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':
ggsave
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).
Version | Author | Date |
---|---|---|
0537a72 | Briana Mittleman | 2019-03-11 |
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.
names=c("SNP", "peak", "pval")
NucQTLinTot=read.table("../data/QTL_overlap/NucQTLs_inTotFractionRes.txt", stringsAsFactors = F, col.names = names)
TotQTLinNuc=read.table("../data/QTL_overlap/TotQTLs_inNucFractionRes.txt", stringsAsFactors = F, col.names = names)
QTL_names= c("pid", "nvar", "shape1", "shape2", "dummy", "SNP", "dist", "npval", "slope", "ppval", "bpval", "bh")
NucQTL=read.table("../data/ApaQTLs/NuclearapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt", col.names = QTL_names, stringsAsFactors = F) %>% separate(pid, into=c("chr", "start","end", "peakID"), sep=":") %>% separate(peakID, into=c("gene", "strand", "peak"), sep="_") %>% select(SNP, peak, ppval)
TotQTL=read.table("../data/ApaQTLs/TotalapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt", col.names = QTL_names, stringsAsFactors = F) %>% separate(pid, into=c("chr", "start","end", "peakID"), sep=":") %>% separate(peakID, into=c("gene", "strand", "peak"), sep="_") %>% select(SNP, peak, ppval)
Join these:
TotQTL_bothfrac= TotQTL %>% inner_join(TotQTLinNuc, by=c("SNP", "peak"))
colnames(TotQTL_bothfrac)= c("SNP", "peak", "Total", "Nuclear")
NucQTL_bothfrac= NucQTL %>% inner_join(NucQTLinTot, by=c("SNP", "peak"))
colnames(NucQTL_bothfrac)= c("SNP", "peak", "Nuclear", "Total")
All qtls together:
allQTL=TotQTL_bothfrac %>% bind_rows(NucQTL_bothfrac)
ggplot(allQTL, aes(x=Nuclear, y=Total)) + geom_point()
The pvalues are not consistent. I need to get the original pvalues from the nominal files.
nominal associations for each QTL:
getNominal4QTLs.py
nucQTLs="/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort.bed"
totQTLs="/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort.bed"
nucNom="/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"
totNom="/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"
outtot="/project2/gilad/briana/threeprimeseq/data/NucSpecQTL/TotQTLs_inTotFractionRes.txt"
outnuc="/project2/gilad/briana/threeprimeseq/data/NucSpecQTL/NucQTLs_inNucFractionRes.txt"
def sameFract(inRes, inQTL, out):
fout=open(out, "w")
qtl_dic={}
#SNP is key, peak is value
for ln in open(inQTL,"r"):
snp=ln.split()[2]
chrom=ln.split()[0]
peak=ln.split()[3].split(":")[0]
qtl=str(chrom) + ":" + str(snp)
if qtl not in qtl_dic.keys():
qtl_dic[qtl]=[peak]
else:
qtl_dic[qtl].append(peak)
#print(qtl_dic)
for ln in open(inRes, "r"):
pval=ln.split()[3]
snp=ln.split()[1]
peak=ln.split()[0].split(":")[3].split("_")[-1]
if snp in qtl_dic.keys():
if peak in qtl_dic[snp]:
fout.write("%s\t%s\t%s\n"%(snp, peak, pval))
fout.close()
sameFract(nucNom, nucQTLs,outnuc)
sameFract(totNom, totQTLs, outtot)
run_getNominal4QTLs.sh
#!/bin/bash
#SBATCH --job-name=run_getNominal4QTLs
#SBATCH --account=pi-yangili1
#SBATCH --time=5:00:00
#SBATCH --output=run_getNominal4QTLs.out
#SBATCH --error=run_getNominal4QTLs.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
python getNominal4QTLs.py
I now want to plot the total vs nuclear nominal pvalues for the nuclear QTLs.
colnames(NucQTLinTot)=c("SNP", "peak", "totalPval")
NucQTLinNuc=read.table("../data/NucSpecQTL/NucQTLs_inNucFractionRes.txt", col.names = c("SNP", "peak","nuclearPval"), stringsAsFactors = F)
NucQTL_all= NucQTLinTot %>% inner_join(NucQTLinNuc, by=c("SNP", "peak"))
summary(lm(data=NucQTL_all,-log10(totalPval) ~ -log10(nuclearPval)))
Call:
lm(formula = -log10(totalPval) ~ -log10(nuclearPval), data = NucQTL_all)
Residuals:
Min 1Q Median 3Q Max
-4.1638 -2.7988 -0.9846 1.5394 17.9462
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 4.1767 0.1719 24.3 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.824 on 494 degrees of freedom
bothPval_nucqtl=ggplot(NucQTL_all, aes(x=-log10(totalPval), y=-log10(nuclearPval))) + geom_point() + geom_density2d(na.rm = TRUE, size = 1, colour = 'red') + geom_smooth(aes(x=-log10(totalPval),y=-log10(nuclearPval)),method = "lm") + labs(title="Nominal Pvalues for Nuclear APA qtls")
#+geom_text(aes(label=SNP),hjust=0, vjust=0)
bothPval_nucqtl
ggplot(NucQTL_all, aes(x=totalPval, y=nuclearPval)) + geom_point() + labs(title="Nominal Pval results for Nuclear QTLs") + geom_density2d(na.rm = TRUE, size = 1, colour = 'red') +geom_vline(xintercept=.05)
nNucQTLNotSigTot= NucQTL_all %>% filter(totalPval>.05) %>% nrow()
nNucQTLNotSigTot
[1] 118
For Total QTLs:
colnames(TotQTLinNuc)=c("SNP", "peak", "nuclearPval")
TotQTLinTot=read.table("../data/NucSpecQTL/TotQTLs_inTotFractionRes.txt", col.names = c("SNP", "peak","totalPval"), stringsAsFactors = F)
TotQTL_all= TotQTLinNuc %>% inner_join(TotQTLinTot, by=c("SNP", "peak"))
summary(lm(data=NucQTL_all,-log10(nuclearPval) ~ -log10(totalPval)))
Call:
lm(formula = -log10(nuclearPval) ~ -log10(totalPval), data = NucQTL_all)
Residuals:
Min 1Q Median 3Q Max
-3.4270 -1.9985 -1.1978 0.9371 15.8122
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 7.1208 0.1385 51.41 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.082 on 494 degrees of freedom
bothPval_totqtl=ggplot(TotQTL_all, aes(x=-log10(nuclearPval), y=-log10(totalPval))) + geom_point() + geom_density2d(na.rm = TRUE, size = 1, colour = 'red') + geom_smooth(aes(x=-log10(nuclearPval),y=-log10(totalPval)),method = "lm") + labs(title="Nominal Pvalues for Total APA qtls")
#+geom_text(aes(label=SNP),hjust=0, vjust=0)
bothPval_totqtl
bothFracPlots=plot_grid(bothPval_nucqtl,bothPval_totqtl)
bothFracPlots
ggsave(bothFracPlots, file="../output/plots/NominalPvalcondQTL.png", height=7, width=12 )
ggplot(TotQTL_all, aes(x=nuclearPval, y=totalPval)) + geom_point() + labs(title="Nominal Pval results for Total QTLs") + geom_density2d(na.rm = TRUE, size = 1, colour = 'red')
nTotQTLNotSigNuc= TotQTL_all %>% filter(nuclearPval>.05) %>% nrow()
nTotQTLNotSigNuc
[1] 39
Is the difference significant. More likely to have nuclear specific qlts?
phyper(118, 495, 272, 157, lower.tail = F)
[1] 0.0005207115
Plot for this:
compareSpecQTL=as.data.frame(cbind(fraction=c("Total QTL", "Total QTL","Nuclear QTL","Nuclear QTL"), SigInOther=c("No","Yes","No","Yes"),Value=c(.14,.86 ,.24,.76)))
compareSpecQTL$Value=as.numeric(as.character(compareSpecQTL$Value))
qtls_oppfrac=ggplot(compareSpecQTL,aes(x=fraction, fill=SigInOther, y=Value)) + geom_bar(stat="identity") +scale_fill_manual(values=c("red","grey")) + labs(y="Proportion", title="QTLs by nominal significance in other fraction") + annotate("text", label="p < .0005", y=1.2, x=1.5) + geom_segment(aes(x=1.1, xend=1.9, y=1.1, yend=1.1))
qtls_oppfrac
ggsave(qtls_oppfrac, file="../output/plots/QTLsSiginOppFraction.png")
Saving 7 x 5 in image
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] cowplot_0.9.4 data.table_1.12.0 forcats_0.4.0
[4] stringr_1.4.0 dplyr_0.8.0.1 purrr_0.3.1
[7] readr_1.3.1 tidyr_0.8.3 tibble_2.0.1
[10] ggplot2_3.1.0 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.5 xfun_0.5 haven_2.1.0 lattice_0.20-38
[5] colorspace_1.4-0 generics_0.0.2 htmltools_0.3.6 yaml_2.2.0
[9] rlang_0.3.1 pillar_1.3.1 glue_1.3.0 withr_2.1.2
[13] modelr_0.1.4 readxl_1.3.0 plyr_1.8.4 munsell_0.5.0
[17] gtable_0.2.0 workflowr_1.2.0 cellranger_1.1.0 rvest_0.3.2
[21] evaluate_0.13 labeling_0.3 knitr_1.21 broom_0.5.1
[25] Rcpp_1.0.0 scales_1.0.0 backports_1.1.3 jsonlite_1.6
[29] fs_1.2.6 hms_0.4.2 digest_0.6.18 stringi_1.3.1
[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 MASS_7.3-51.1 xml2_1.2.0 lubridate_1.7.4
[45] assertthat_0.2.0 rmarkdown_1.11 httr_1.4.0 rstudioapi_0.9.0
[49] R6_2.4.0 nlme_3.1-137 git2r_0.24.0 compiler_3.5.1