Last updated: 2019-02-15
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Knit directory: threeprimeseq/analysis/
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Unstaged changes:
Modified: analysis/28ind.peak.explore.Rmd
Modified: analysis/CompareLianoglouData.Rmd
Modified: analysis/NewPeakPostMP.Rmd
Modified: analysis/apaQTLoverlapGWAS.Rmd
Modified: analysis/cleanupdtseq.internalpriming.Rmd
Modified: analysis/coloc_apaQTLs_protQTLs.Rmd
Modified: analysis/dif.iso.usage.leafcutter.Rmd
Modified: analysis/diff_iso_pipeline.Rmd
Modified: analysis/explainpQTLs.Rmd
Modified: analysis/explore.filters.Rmd
Modified: analysis/flash2mash.Rmd
Modified: analysis/mispriming_approach.Rmd
Modified: analysis/overlapMolQTL.Rmd
Modified: analysis/overlapMolQTL.opposite.Rmd
Modified: analysis/overlap_qtls.Rmd
Modified: analysis/peakOverlap_oppstrand.Rmd
Modified: analysis/peakQCPPlots.Rmd
Modified: analysis/pheno.leaf.comb.Rmd
Modified: analysis/pipeline_55Ind.Rmd
Modified: analysis/swarmPlots_QTLs.Rmd
Modified: analysis/test.max2.Rmd
Modified: analysis/test.smash.Rmd
Modified: analysis/understandPeaks.Rmd
Modified: code/Snakefile
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File | Version | Author | Date | Message |
---|---|---|---|---|
html | b68140c | brimittleman | 2018-08-23 | Build site. |
Rmd | f5c2ce2 | brimittleman | 2018-08-23 | work more on locus zoom prob |
html | b33443c | brimittleman | 2018-08-23 | Build site. |
Rmd | c1f8d60 | brimittleman | 2018-08-23 | add qtc characteristics |
html | 886dc9a | brimittleman | 2018-08-23 | Build site. |
Rmd | dd07e10 | brimittleman | 2018-08-23 | box plot for top snps |
html | c8f2c7d | brimittleman | 2018-08-22 | Build site. |
Rmd | 0fbf10b | brimittleman | 2018-08-22 | work on plotting top QTL |
html | bd21c34 | brimittleman | 2018-08-21 | Build site. |
Rmd | 5ffffe1 | brimittleman | 2018-08-21 | BH result plots |
html | b6e6ed9 | brimittleman | 2018-08-21 | Build site. |
Rmd | 73516a6 | brimittleman | 2018-08-21 | chr1 results |
html | d682ab6 | brimittleman | 2018-08-21 | Build site. |
Rmd | a3c44fb | brimittleman | 2018-08-21 | add code for permute fastqtl |
html | 5564e25 | brimittleman | 2018-08-20 | Build site. |
Rmd | 6b1b51c | brimittleman | 2018-08-20 | start qtl analsis, add to index |
I need to run fastQTL to call the apaQTLs.
Imputed snp: /project2/yangili1/tonyzeng/genotyping/imputation_results/ `
module load samtools
#zip file
gzip filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt
module load python
#leafcutter script
python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz
#source activate three-prime-env
sh filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz_prepare.sh
#run for nuclear as well
gzip filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt
#unload anaconda, load python
python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz
#load anaconda and env.
sh filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz_prepare.sh
#filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.PCs
#filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.PCs
makeSamplelist.py
#make a sample list
fout = file("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/SAMPLE.txt",'w')
for ln in open("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/file_id_mapping_nuc.txt", "r"):
bam, sample = ln.split()
line=sample[:-2]
fout.write("NA"+line + "\n")
fout.close()
APAqtl_nominal_nuc.sh
#!/bin/bash
#SBATCH --job-name=APAqtl_nominal_nuc
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_nominal_nuc.out
#SBATCH --error=APAqtl_nominal_nuc.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.qqnorm_chr$i.nominal.out --chunk 1 1 --window 5e4 --include-samples /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/SAMPLE.txt
done
Remove the non matching ind. from the sample list.
Remove 18500, 19092 and 19193, 18497
Try it on the total ones:
APAqtl_nominal_tot.sh
#!/bin/bash
#SBATCH --job-name=APAqtl_nominal_tot
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_nominal_tot.out
#SBATCH --error=APAqtl_nominal_tot.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.qqnorm_chr$i.nominal.out --chunk 1 1 --window 5e4 --include-samples /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/SAMPLE.txt
done
I need to remove non snps and snps with <.05 from the dosage file.
I will first copy all of the dosage files to my direcory instead of changing tonys.
cp *dose.vcf.gz /project2/gilad/briana/YRI_geno_hg19/
I want to write a python script that will read in the files and perform the filters.
I wrote a python script that take in the dose file and a name of an out file. I will write a bash script to wrap this on all of the chrs.
#!/bin/bash
#SBATCH --job-name=filter_dose
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=filter_dose.out
#SBATCH --error=filter_dose.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load python
for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
do
python filter_vcf.py chr$i.dose.vcf chr$i.dose.filt.vcf
done
Now I can use these for the fastqtl script instead.
I also updated to only use the first 2 pcs as covariates.
Permutation pass to calculate correctedp-values for molecular phenotypes.
APAqtl_perm_tot.sh
#!/bin/bash
#SBATCH --job-name=APAqtl_perm_tot
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_perm_tot.out
#SBATCH --error=APAqtl_perm_tot.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --permute 1000 --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.qqnorm_chr$i.perm.out --chunk 1 1 --window 5e4 --include-samples /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/SAMPLE.txt
done
APAqtl_perm_nuc.sh
#!/bin/bash
#SBATCH --job-name=APAqtl_nominal_nuc
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_perm_nuc.out
#SBATCH --error=APAqtl_perm_nuc.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --permute 1000 --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.qqnorm_chr$i.perm.out --chunk 1 1 --window 5e4 --include-samples /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/SAMPLE.txt
done
Try with normal approximation for the chroms that dont work:
APAqtl_perm_norm_tot.sh
#!/bin/bash
#SBATCH --job-name=APAqtl_perm_tot
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_perm_tot.out
#SBATCH --error=APAqtl_perm_tot.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
for i in 13 18
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --permute 1000 --normal --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.qqnorm_chr$i.perm.norm.out --chunk 1 1 --window 5e4 --include-samples /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/SAMPLE.txt
done
APAqtl_perm_norm_nuc.sh
#!/bin/bash
#SBATCH --job-name=APAqtl_nominal_nuc
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_perm_nuc.out
#SBATCH --error=APAqtl_perm_nuc.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
for i in 3 13
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --permute 1000 --normal --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.qqnorm_chr$i.perm.norm.out --chunk 1 1 --window 5e4 --include-samples /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/SAMPLE.txt
done
The results file has the folowing columns:
I can check the experiments as recomended by the FastQTL site.
d = read.table("permutations.all.chunks.txt.gz", hea=F, stringsAsFactors=F)
colnames(d) = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "ppval", "bpval")
plot(d$ppval, d$bpval, xlab="Direct method", ylab="Beta approximation", main="Check plot")
abline(0, 1, col="red")
I will try this first on the resutls from chr1.
nuc.chr1= read.table("../data/perm_QTL/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.qqnorm_chr1.perm.out",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
plot(nuc.chr1$ppval, nuc.chr1$bpval, xlab="Direct method", ylab="Beta approximation", main="Nuclear Check plot")
abline(0, 1, col="red")
tot.chr1=read.table("../data/perm_QTL/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.qqnorm_chr1.perm.out", head=F, stringsAsFactors = F, col.names= c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
plot(tot.chr1$ppval, tot.chr1$bpval, xlab="Direct method", ylab="Beta approximation", main="Total Check plot")
abline(0, 1, col="red")
Correct for multiple testing:
nuc.chr1$bonferroni = p.adjust(nuc.chr1$bpval, method="bonferroni")
plot(-log10(nuc.chr1$bonferroni), main="Nuclear chr1 bonferroni corrected pval")
tot.chr1$bonferroni = p.adjust(tot.chr1$bpval, method="bonferroni")
plot(-log10(tot.chr1$bonferroni), main="Total chr1 bonferroni corrected pval")
< .05 is 1.3 on this plot.
nuc.chr1$bh=p.adjust(nuc.chr1$bpval, method="fdr")
plot(-log10(nuc.chr1$bh), main="Nuclear chr1 BH corrected pval")
tot.chr1$bh=p.adjust(tot.chr1$bpval, method="fdr")
plot(-log10(tot.chr1$bh), main="Total chr1 BH corrected pval")
10% FDR is 1 on this plot.
Extend to all results:
nuc.res= read.table("../data/perm_QTL/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_permQTLresults.out",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
plot(nuc.res$ppval, nuc.res$bpval, xlab="Direct method", ylab="Beta approximation", main="Nuclear Check plot")
abline(0, 1, col="red")
Version | Author | Date |
---|---|---|
c8f2c7d | brimittleman | 2018-08-22 |
tot.res=read.table("../data/perm_QTL/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_permQTLresults.out", head=F, stringsAsFactors = F, col.names= c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
plot(tot.res$ppval, tot.res$bpval, xlab="Direct method", ylab="Beta approximation", main="Total Check plot")
abline(0, 1, col="red")
Version | Author | Date |
---|---|---|
c8f2c7d | brimittleman | 2018-08-22 |
nuc.res$bh=p.adjust(nuc.res$bpval, method="fdr")
plot(-log10(nuc.res$bh), main="Nuclear BH corrected pval")
abline(h=1, col="red")
Version | Author | Date |
---|---|---|
c8f2c7d | brimittleman | 2018-08-22 |
tot.res$bh=p.adjust(tot.res$bpval, method="fdr")
plot(-log10(tot.res$bh), main="Total BH corrected pval")
abline(h=1, col="red")
Version | Author | Date |
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c8f2c7d | brimittleman | 2018-08-22 |
Next steps:
make plots for some of these snps
library(tidyverse)
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✔ 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(workflowr)
This is workflowr version 1.2.0
Run ?workflowr for help getting started
library(reshape2)
Attaching package: 'reshape2'
The following object is masked from 'package:tidyr':
smiths
library(cowplot)
Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':
ggsave
ceu_QTL=read.table("../data/nom_QTL/ceu.apaqtl.txt.gz.bh.txt", header = T, stringsAsFactors = F)
nom_nuc=read.table("../data/nom_QTL/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_nomQTLresults.out", head=F, stringsAsFactors = F, col.names = c("peakID", "snpID", "dist", "Nuc_pval", "slope"))
nom_tot=read.table("../data/nom_QTL/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_nomQTLresults.out",head=F , stringsAsFactors = F, col.names = c("peakID", "snpID", "dist", "tot_pval", "slope"))
First I want to filter the CEU data just for snps. Then I want to reformat them to be in the same configuration as the nps in my results.
chr#:pos
ceu_QTL_snp=ceu_QTL %>% filter(grepl("snp", dummy2)) %>% separate(dummy2, c("type", "chr", "loc"), sep="_") %>% unite(snpID, c("chr", "loc"), sep=":")
Join the data frames by the snp ID.
ceuAndTot= ceu_QTL_snp %>% inner_join(nom_tot, by="snpID") %>% select(snpID, bpval, tot_pval)
ceuAndNuc= ceu_QTL_snp %>% inner_join(nom_nuc, by="snpID") %>% select(snpID, bpval, Nuc_pval)
tot_ceuSNPS=runif(nrow(ceuAndTot))
nuc_ceuSNPS=runif(nrow(ceuAndNuc))
par(mfrow=c(1,2))
qqplot(-log10(tot_ceuSNPS), -log10(ceuAndTot$tot_pval), ylab="-log10 Total pvalues", xlab="Uniform expectation", main="Total pvalues for in CEU snps")
abline(0,1)
qqplot(-log10(nuc_ceuSNPS), -log10(ceuAndNuc$Nuc_pva), ylab="-log10 Nuclear pvalues", xlab="Uniform expectation", main="Nuclear pvalues for in CEU snps")
abline(0,1)
Version | Author | Date |
---|---|---|
c8f2c7d | brimittleman | 2018-08-22 |
Try with all of the snps:
par(mfrow=c(1,2))
qqplot(-log10(runif(nrow(nom_tot))), -log10(nom_tot$tot_pval), ylab="-log10 Total pvalue", xlab="Uniform expectation", main="Total pvalues for all snps")
abline(0,1)
qqplot(-log10(runif(nrow(nom_nuc))), -log10(nom_nuc$Nuc_pval), ylab="-log10 Nuclear pvalue", xlab="Uniform expectation",main= "Nuclear pvalues for all snps")
abline(0,1)
Version | Author | Date |
---|---|---|
c8f2c7d | brimittleman | 2018-08-22 |
Try this with te permuted pvalues:
par(mfrow=c(1,2))
qqplot(-log10(runif(nrow(tot.res))), -log10(tot.res$bpval),ylab="-log10 Total permuted pvalue", xlab="Uniform expectation", main="Total permuted pvalues for all snps")
abline(0,1)
qqplot(-log10(runif(nrow(nuc.res))), -log10(nuc.res$bpval), ylab="-log10 Nuclear permuted pvalue", xlab="Uniform expectation", main="Nuclear permuted pvalues for all snps")
abline(0,1)
Version | Author | Date |
---|---|---|
c8f2c7d | brimittleman | 2018-08-22 |
Locus zoom plots to vizualize the top QTLs:
Kenneth gave me this code for making these plots. I can modify this code.
plot_locuszoom <- function(this, gen, xlim, ylim, ...)
{
#this is a r object that will have the results from the fastqtl and the genotypes
#this$annotations has gene, snp, dist, pvalue, beta, rsid, chr, pos, bpval, and other extra annotations about the snps
rbPal <- colorRampPalette(c('lightblue','blue','purple','red'))(101)
cols <- c()
# gotta figure out how everythign correlates with this snp
# row <- which(this$annotations$rsid==snp)
# gen <- as.numeric(this$genotypes[row,10:129])
nrow <- nrow(this$annotations)
cors <- sapply(1:nrow, function(j) cor(gen, as.numeric(this$genotypes[j,10:33])))
cols <- c()
for (j in 1:nrow) cols[j] <- rbPal[round(100*(cors[j])^2)+1]
plot.new()
plot.window(xlim=xlim, ylim=ylim, xlab='position', ylab='-log10(p-value)', ...)
points(x=this$annotations$pos, y=-log(this$annotations$bpval,10), pch=19, col=cols)
axis(2)
box()
mtext('-log10(p-value)', side=2, line=2, cex=0.7)
}
I will try this with the top total snp first. It is in chrom15, the snip id is 15:76191353. I want to pull genotypes for snp within 50000 bases (window size).
I can write a python script that takes a snp position and filters only the snps 25000 up and 25000 downstream of this snp. I can subset just the individuals in the sample list once i move this into R.
Need to make sure to unzip the specfici vcf file first.
python filter_geno.py 15 76191353 /project2/gilad/briana/threeprimeseq/data/filtered_geno/chrom15pos76191353.vcf
samples=c("NA18486","NA18505", 'NA18508','NA18511','NA18519','NA18520','NA18853','NA18858','NA18861','NA18870','NA18909','NA18916','NA19119','NA19128','NA19130','NA19141','NA19160','NA19209','NA19210','NA19223','NA19225','NA19238','NA19239','NA19257')
chr15.76191353geno=read.table("../data/perm_QTL/chrom15pos76191353.vcf", col.names=c('CHROM', 'POS', 'snpID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', 'NA18486', 'NA18487', 'NA18488', 'NA18489', 'NA18498', 'NA18499', 'NA18501', 'NA18502', 'NA18504', 'NA18505', 'NA18507', 'NA18508', 'NA18510', 'NA18511', 'NA18516', 'NA18517', 'NA18519', 'NA18520', 'NA18522', 'NA18523', 'NA18852', 'NA18853', 'NA18855', 'NA18856', 'NA18858', 'NA18859', 'NA18861', 'NA18862', 'NA18867', 'NA18868', 'NA18870', 'NA18871', 'NA18873', 'NA18874', 'NA18907', 'NA18909', 'NA18910', 'NA18912', 'NA18913', 'NA18916', 'NA18917', 'NA18923', 'NA18924', 'NA18933', 'NA18934', 'NA19093', 'NA19095', 'NA19096', 'NA19098', 'NA19099', 'NA19101', 'NA19102', 'NA19107', 'NA19108', 'NA19113', 'NA19114', 'NA19116', 'NA19117', 'NA19118', 'NA19119', 'NA19121', 'NA19122', 'NA19127', 'NA19128', 'NA19129', 'NA19130', 'NA19131', 'NA19137', 'NA19138', 'NA19140', 'NA19141', 'NA19143', 'NA19144', 'NA19146', 'NA19147', 'NA19149', 'NA19150', 'NA19152', 'NA19153', 'NA19159', 'NA19160', 'NA19171', 'NA19172', 'NA19175', 'NA19176', 'NA19184', 'NA19185', 'NA19189', 'NA19190', 'NA19197', 'NA19198', 'NA19200', 'NA19201', 'NA19203', 'NA19204', 'NA19206', 'NA19207', 'NA19209', 'NA19210', 'NA19213', 'NA19214', 'NA19222', 'NA19223', 'NA19225', 'NA19226', 'NA19235', 'NA19236', 'NA19238', 'NA19239', 'NA19247', 'NA19248', 'NA19256', 'NA19257'), stringsAsFactors = F) %>% select(one_of(samples))
chr15.76191353geno_anno=read.table("../data/perm_QTL/chrom15pos76191353.vcf", col.names=c('CHROM', 'POS', 'snpID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', 'NA18486', 'NA18487', 'NA18488', 'NA18489', 'NA18498', 'NA18499', 'NA18501', 'NA18502', 'NA18504', 'NA18505', 'NA18507', 'NA18508', 'NA18510', 'NA18511', 'NA18516', 'NA18517', 'NA18519', 'NA18520', 'NA18522', 'NA18523', 'NA18852', 'NA18853', 'NA18855', 'NA18856', 'NA18858', 'NA18859', 'NA18861', 'NA18862', 'NA18867', 'NA18868', 'NA18870', 'NA18871', 'NA18873', 'NA18874', 'NA18907', 'NA18909', 'NA18910', 'NA18912', 'NA18913', 'NA18916', 'NA18917', 'NA18923', 'NA18924', 'NA18933', 'NA18934', 'NA19093', 'NA19095', 'NA19096', 'NA19098', 'NA19099', 'NA19101', 'NA19102', 'NA19107', 'NA19108', 'NA19113', 'NA19114', 'NA19116', 'NA19117', 'NA19118', 'NA19119', 'NA19121', 'NA19122', 'NA19127', 'NA19128', 'NA19129', 'NA19130', 'NA19131', 'NA19137', 'NA19138', 'NA19140', 'NA19141', 'NA19143', 'NA19144', 'NA19146', 'NA19147', 'NA19149', 'NA19150', 'NA19152', 'NA19153', 'NA19159', 'NA19160', 'NA19171', 'NA19172', 'NA19175', 'NA19176', 'NA19184', 'NA19185', 'NA19189', 'NA19190', 'NA19197', 'NA19198', 'NA19200', 'NA19201', 'NA19203', 'NA19204', 'NA19206', 'NA19207', 'NA19209', 'NA19210', 'NA19213', 'NA19214', 'NA19222', 'NA19223', 'NA19225', 'NA19226', 'NA19235', 'NA19236', 'NA19238', 'NA19239', 'NA19247', 'NA19248', 'NA19256', 'NA19257'), stringsAsFactors = F) %>% select(CHROM, POS, snpID, REF, ALT, QUAL, FILTER, INFO, FORMAT)
chr15.76191353geno_dose=apply(chr15.76191353geno, 2, function(y)sapply(y, function(x)as.integer(strsplit(x,":")[[1]][[2]])))
chr15.76191353geno_dose_full=data.frame(cbind(chr15.76191353geno_anno, chr15.76191353geno_dose))
gen=chr15.76191353geno_dose_full[which(chr15.76191353geno_dose_full$POS==76191353),]
gen
CHROM POS snpID REF ALT QUAL FILTER
84 15 76191353 15:76191353 C T . PASS
INFO FORMAT NA18486 NA18505 NA18508
84 AF=0.08407;MAF=0.08407;R2=0.99998 GT:DS:GP 0 0 1
NA18511 NA18519 NA18520 NA18853 NA18858 NA18861 NA18870 NA18909 NA18916
84 0 0 0 1 0 0 1 0 0
NA19119 NA19128 NA19130 NA19141 NA19160 NA19209 NA19210 NA19223 NA19225
84 0 0 0 0 0 0 0 0 0
NA19238 NA19239 NA19257
84 0 0 0
snps=chr15.76191353geno_dose_full$snpID
in_both_nom= nom_tot %>% filter(snpID %in% snps)
mylist=list(annotations=tot.res,genotypes=chr15.76191353geno_dose_full )
start=76191353 - 25000
end=76191353 + 25000
#plot_locuszoom(mylist, gen, start, end)
I actually need to do this with the nominal snps.
The most sig. in the nominal total is 4:186328829:186328922:NM_018359.3_-_peak260565, 4:186325141
I want to run the python genotype filter.
python filter_geno.py 4 186325141 /project2/gilad/briana/threeprimeseq/data/filtered_geno/chrom4pos186325141.vcf
chrom4pos18632514=read.table("../data/nom_QTL/chrom4pos186325141.vcf", col.names=c('CHROM', 'POS', 'snpID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', 'NA18486', 'NA18487', 'NA18488', 'NA18489', 'NA18498', 'NA18499', 'NA18501', 'NA18502', 'NA18504', 'NA18505', 'NA18507', 'NA18508', 'NA18510', 'NA18511', 'NA18516', 'NA18517', 'NA18519', 'NA18520', 'NA18522', 'NA18523', 'NA18852', 'NA18853', 'NA18855', 'NA18856', 'NA18858', 'NA18859', 'NA18861', 'NA18862', 'NA18867', 'NA18868', 'NA18870', 'NA18871', 'NA18873', 'NA18874', 'NA18907', 'NA18909', 'NA18910', 'NA18912', 'NA18913', 'NA18916', 'NA18917', 'NA18923', 'NA18924', 'NA18933', 'NA18934', 'NA19093', 'NA19095', 'NA19096', 'NA19098', 'NA19099', 'NA19101', 'NA19102', 'NA19107', 'NA19108', 'NA19113', 'NA19114', 'NA19116', 'NA19117', 'NA19118', 'NA19119', 'NA19121', 'NA19122', 'NA19127', 'NA19128', 'NA19129', 'NA19130', 'NA19131', 'NA19137', 'NA19138', 'NA19140', 'NA19141', 'NA19143', 'NA19144', 'NA19146', 'NA19147', 'NA19149', 'NA19150', 'NA19152', 'NA19153', 'NA19159', 'NA19160', 'NA19171', 'NA19172', 'NA19175', 'NA19176', 'NA19184', 'NA19185', 'NA19189', 'NA19190', 'NA19197', 'NA19198', 'NA19200', 'NA19201', 'NA19203', 'NA19204', 'NA19206', 'NA19207', 'NA19209', 'NA19210', 'NA19213', 'NA19214', 'NA19222', 'NA19223', 'NA19225', 'NA19226', 'NA19235', 'NA19236', 'NA19238', 'NA19239', 'NA19247', 'NA19248', 'NA19256', 'NA19257'), stringsAsFactors = F) %>% select(one_of(samples))
chrom4pos18632514_anno=read.table("../data/nom_QTL/chrom4pos186325141.vcf", col.names=c('CHROM', 'POS', 'snpID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', 'NA18486', 'NA18487', 'NA18488', 'NA18489', 'NA18498', 'NA18499', 'NA18501', 'NA18502', 'NA18504', 'NA18505', 'NA18507', 'NA18508', 'NA18510', 'NA18511', 'NA18516', 'NA18517', 'NA18519', 'NA18520', 'NA18522', 'NA18523', 'NA18852', 'NA18853', 'NA18855', 'NA18856', 'NA18858', 'NA18859', 'NA18861', 'NA18862', 'NA18867', 'NA18868', 'NA18870', 'NA18871', 'NA18873', 'NA18874', 'NA18907', 'NA18909', 'NA18910', 'NA18912', 'NA18913', 'NA18916', 'NA18917', 'NA18923', 'NA18924', 'NA18933', 'NA18934', 'NA19093', 'NA19095', 'NA19096', 'NA19098', 'NA19099', 'NA19101', 'NA19102', 'NA19107', 'NA19108', 'NA19113', 'NA19114', 'NA19116', 'NA19117', 'NA19118', 'NA19119', 'NA19121', 'NA19122', 'NA19127', 'NA19128', 'NA19129', 'NA19130', 'NA19131', 'NA19137', 'NA19138', 'NA19140', 'NA19141', 'NA19143', 'NA19144', 'NA19146', 'NA19147', 'NA19149', 'NA19150', 'NA19152', 'NA19153', 'NA19159', 'NA19160', 'NA19171', 'NA19172', 'NA19175', 'NA19176', 'NA19184', 'NA19185', 'NA19189', 'NA19190', 'NA19197', 'NA19198', 'NA19200', 'NA19201', 'NA19203', 'NA19204', 'NA19206', 'NA19207', 'NA19209', 'NA19210', 'NA19213', 'NA19214', 'NA19222', 'NA19223', 'NA19225', 'NA19226', 'NA19235', 'NA19236', 'NA19238', 'NA19239', 'NA19247', 'NA19248', 'NA19256', 'NA19257'), stringsAsFactors = F) %>% select(CHROM, POS, snpID, REF, ALT, QUAL, FILTER, INFO, FORMAT)
chrom4pos18632514_dose=apply(chrom4pos18632514, 2, function(y)sapply(y, function(x)as.integer(strsplit(x,":")[[1]][[2]])))
chrom4pos18632514_dose_full=data.frame(cbind(chrom4pos18632514_anno, chrom4pos18632514_dose))
snps=chrom4pos18632514_dose_full$snpID
in_both_nom= nom_tot %>% filter(snpID %in% snps)
gen=chrom4pos18632514_dose_full[which(chrom4pos18632514_dose_full$POS==186325141),]
mylist=list(annotations=in_both_nom,genotypes=chrom4pos18632514_dose_full)
start=18632514- 25000
end=18632514 + 25000
#plot_locuszoom(mylist, gen, start, end)
#problem: the in_both_nom has more values because snps can be associated with more than one peak w
Try to make a boxplot:
FIrst for the strongest total pval.
geno=chr15.76191353geno_dose_full[which(chr15.76191353geno_dose_full$POS==76191353),10:33]
# find the phentpye values for peak 15:76234771:76234852:NM_138573.3_-_peak118132
#grep -F "15:76234771:76234852:NM_138573.3_-_peak118132" filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.phen_chr15 > ../qtl_example/tot_peak118132
pheno=read.table("../data/perm_QTL/tot_peak118132", stringsAsFactors = F, col.names = c("Chr", "start", "end", "ID", 'NA18486', 'NA18497', 'NA18500', 'NA18505','NA18508' ,'NA18511', 'NA18519', 'NA18520', 'NA18853', 'NA18858', 'NA18861' ,'NA18870', 'NA18909', 'NA18916', 'NA19092', 'NA19119', 'NA19128' ,'NA19130', 'NA19141' ,'NA19160', 'NA19193', 'NA19209' ,'NA19210', 'NA19223' ,'NA19225', 'NA19238', 'NA19239' ,'NA19257')) %>% select(one_of(samples))
for_plot=data.frame(bind_rows(geno,pheno) %>% t)
colnames(for_plot)=c("Genotype", "PAS")
for_plot$Genotype=as.factor(for_plot$Genotype)
ggplot(for_plot, aes(x=Genotype, y=PAS, fill=Genotype, group=Genotype)) + geom_boxplot() + labs(x="Genotype", title="15:76234771:76234852:NM_138573.3_-_peak118132 QTL") + geom_jitter( aes(x=Genotype, y=PAS))
Version | Author | Date |
---|---|---|
b68140c | brimittleman | 2018-08-23 |
Generally I will need to grep the correct line from the geno and pheno file then make the plot like this.
Next I will run for the top Nuc QTL.
peak: 12:9092958:9093051:NM_004426.2_+_peak67056 SNP: 12:9049821
#grep -F "12:9092958:9093051:NM_004426.2_+_peak67056" filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.phen_chr12 > ../qtl_example/nuc_peak67056
pheno_names=c("Chr", "start", "end", "ID", 'NA18486', 'NA18497', 'NA18500', 'NA18505','NA18508' ,'NA18511', 'NA18519', 'NA18520', 'NA18853', 'NA18858', 'NA18861' ,'NA18870', 'NA18909', 'NA18916', 'NA19092', 'NA19119', 'NA19128' ,'NA19130', 'NA19141' ,'NA19160', 'NA19193', 'NA19209' ,'NA19210', 'NA19223' ,'NA19225', 'NA19238', 'NA19239' ,'NA19257')
geno_names=c('CHROM', 'POS', 'sid', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', 'NA18486', 'NA18487', 'NA18488', 'NA18489', 'NA18498', 'NA18499', 'NA18501', 'NA18502', 'NA18504', 'NA18505', 'NA18507', 'NA18508', 'NA18510', 'NA18511', 'NA18516', 'NA18517', 'NA18519', 'NA18520', 'NA18522', 'NA18523', 'NA18852', 'NA18853', 'NA18855', 'NA18856', 'NA18858', 'NA18859', 'NA18861', 'NA18862', 'NA18867', 'NA18868', 'NA18870', 'NA18871', 'NA18873', 'NA18874', 'NA18907', 'NA18909', 'NA18910', 'NA18912', 'NA18913', 'NA18916', 'NA18917', 'NA18923', 'NA18924', 'NA18933', 'NA18934', 'NA19093', 'NA19095', 'NA19096', 'NA19098', 'NA19099', 'NA19101', 'NA19102', 'NA19107', 'NA19108', 'NA19113', 'NA19114', 'NA19116', 'NA19117', 'NA19118', 'NA19119', 'NA19121', 'NA19122', 'NA19127', 'NA19128', 'NA19129', 'NA19130', 'NA19131', 'NA19137', 'NA19138', 'NA19140', 'NA19141', 'NA19143', 'NA19144', 'NA19146', 'NA19147', 'NA19149', 'NA19150', 'NA19152', 'NA19153', 'NA19159', 'NA19160', 'NA19171', 'NA19172', 'NA19175', 'NA19176', 'NA19184', 'NA19185', 'NA19189', 'NA19190', 'NA19197', 'NA19198', 'NA19200', 'NA19201', 'NA19203', 'NA19204', 'NA19206', 'NA19207', 'NA19209', 'NA19210', 'NA19213', 'NA19214', 'NA19222', 'NA19223', 'NA19225', 'NA19226', 'NA19235', 'NA19236', 'NA19238', 'NA19239', 'NA19247', 'NA19248', 'NA19256', 'NA19257')
top_nuc_geno=read.table("../data/perm_QTL/genotpye12:904921", stringsAsFactors = F, col.names = geno_names) %>% select(one_of(samples))
top_nuc_geno_dose=apply(top_nuc_geno, 2, function(y)sapply(y, function(x)as.integer(strsplit(x,":")[[1]][[2]])))
top_nuc_pheo=read.table("../data/perm_QTL/nuc_peak67056", stringsAsFactors = F, col.names = pheno_names) %>% select(one_of(samples))
top_nuc_plot=data.frame(bind_rows(top_nuc_geno_dose, top_nuc_pheo) %>% t)
colnames(top_nuc_plot)=c("Genotype", "PAS")
top_nuc_plot$Genotype=as.factor(top_nuc_plot$Genotype)
ggplot(top_nuc_plot, aes(x=Genotype, y=PAS, fill=Genotype, group=Genotype)) + geom_boxplot() + labs(x="Genotype", title="12:9092958:9093051:NM_004426.2_+_peak67056") + geom_jitter( aes(x=Genotype, y=PAS))
3:119242427:119242509:NM_016589.3_+_peak233134
3:119211867
grep -F "3:119242427:119242509:NM_016589.3_+_peak233134" filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.phen_chr3 > ../qtl_example/nuc_peak233134
YRI_geno_hg19]$ less chr3.dose.filt.vcf.gz | grep "3:119211867" > ../threeprimeseq/data/qtl_example/genotype3:199211867
top_nuc_geno2=read.table("../data/perm_QTL/genotype3:199211867", stringsAsFactors = F, col.names = geno_names) %>% select(one_of(samples))
top_nuc_geno2_dose=apply(top_nuc_geno2, 2, function(y)sapply(y, function(x)as.integer(strsplit(x,":")[[1]][[2]])))
top_nuc_pheo2=read.table("../data/perm_QTL/nuc_peak233134", stringsAsFactors = F, col.names = pheno_names) %>% select(one_of(samples))
top_nuc_plot2=data.frame(bind_rows(top_nuc_geno2_dose, top_nuc_pheo2) %>% t)
colnames(top_nuc_plot2)=c("Genotype", "PAS")
top_nuc_plot2$Genotype=as.factor(top_nuc_plot2$Genotype)
ggplot(top_nuc_plot2, aes(x=Genotype, y=PAS, fill=Genotype, group=Genotype)) + geom_boxplot() + labs(x="Genotype", title="12:9092958:9093051:NM_004426.2_+_peak67056") + geom_jitter( aes(x=Genotype, y=PAS))
Version | Author | Date |
---|---|---|
b68140c | brimittleman | 2018-08-23 |
I want to look at the distance to the snp for the QTLS
tot_QTL=tot.res %>% filter(bh < .15 )
nuc_QTL= nuc.res %>% filter(bh< .15)
tot.res = tot.res %>% mutate(QTL=ifelse(bh<.15, "Yes", "No") )
nuc.res = nuc.res %>% mutate(QTL=ifelse(bh<.15, "Yes", "No") )
Now I can look at caharacteristics of those that pass the cutoff.
tot.dist=ggplot(tot.res, aes(x=log10(abs(dist)+1), group=QTL, fill=QTL)) + geom_density(alpha=.4) + labs(title="Distribtuion of density in Total QTLS", x="Log 10 abs. values distance from SNP to peaks")
nuc.dist=ggplot(nuc.res, aes(x=log10(abs(dist)+1), group=QTL, fill=QTL)) + geom_density(alpha=.4) + labs(title="Distribtuion of density in Nuclear QTLS",x="Log 10 abs. values distance from SNP to peaks")
plot_grid(tot.dist, nuc.dist)
Version | Author | Date |
---|---|---|
b68140c | brimittleman | 2018-08-23 |
I want to assess the number of QTLs we get at different cutoffs. To do this I will wrap a drplyr function in a for look that goes from .05 to .5.
nQTL_tot=c()
FDR=seq(.05, .5, .01)
for (i in FDR){
x=tot.res %>% filter(bh < i ) %>% nrow()
nQTL_tot=c(nQTL_tot, x)
}
FDR=seq(.05, .5, .01)
nQTL_nuc=c()
for (i in FDR){
x=nuc.res %>% filter(bh < i ) %>% nrow()
nQTL_nuc=c(nQTL_nuc, x)
}
nQTL=as.data.frame(cbind(FDR, Total=nQTL_tot, Nuclear=nQTL_nuc))
nQTL_long=melt(nQTL, id.vars = "FDR")
ggplot(nQTL_long, aes(x=FDR, y=value, by=variable, col=variable)) + geom_line(size=1.5) + labs(y="Number of Significant QTLs", title="APAqtls detected by FDR cuttoff", color="Fraction")
Version | Author | Date |
---|---|---|
b68140c | brimittleman | 2018-08-23 |
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 cowplot_0.9.3 reshape2_1.4.3 workflowr_1.2.0
[5] forcats_0.3.0 stringr_1.4.0 dplyr_0.7.6 purrr_0.2.5
[9] readr_1.1.1 tidyr_0.8.1 tibble_1.4.2 ggplot2_3.0.0
[13] tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] tidyselect_0.2.4 haven_1.1.2 lattice_0.20-35 colorspace_1.3-2
[5] htmltools_0.3.6 yaml_2.2.0 rlang_0.2.2 pillar_1.3.0
[9] glue_1.3.0 withr_2.1.2 modelr_0.1.2 readxl_1.1.0
[13] bindr_0.1.1 plyr_1.8.4 munsell_0.5.0 gtable_0.2.0
[17] cellranger_1.1.0 rvest_0.3.2 evaluate_0.13 labeling_0.3
[21] knitr_1.20 broom_0.5.0 Rcpp_0.12.19 scales_1.0.0
[25] backports_1.1.2 jsonlite_1.6 fs_1.2.6 hms_0.4.2
[29] digest_0.6.17 stringi_1.2.4 grid_3.5.1 rprojroot_1.3-2
[33] cli_1.0.1 tools_3.5.1 magrittr_1.5 lazyeval_0.2.1
[37] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2 xml2_1.2.0
[41] lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.11 httr_1.3.1
[45] rstudioapi_0.9.0 R6_2.3.0 nlme_3.1-137 git2r_0.24.0
[49] compiler_3.5.1