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<h1 class="title toc-ignore">Top APA QTL in other phenotypes</h1>
<h4 class="author"><em>Briana Mittleman</em></h4>
<h4 class="date"><em>10/8/2018</em></h4>
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<p><strong>Last updated:</strong> 2018-10-11</p>
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Unstaged changes:
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<p></details></p>
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<h2>Upload Data:</h2>
<p>Library</p>
<pre class="r"><code>library(workflowr)</code></pre>
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Run ?workflowr for help getting started</code></pre>
<pre class="r"><code>library(reshape2)
library(tidyverse)</code></pre>
<pre><code>── Attaching packages ─────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──</code></pre>
<pre><code>✔ 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</code></pre>
<pre><code>── Conflicts ────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()</code></pre>
<pre class="r"><code>library(VennDiagram)</code></pre>
<pre><code>Loading required package: grid</code></pre>
<pre><code>Loading required package: futile.logger</code></pre>
<pre class="r"><code>library(data.table)</code></pre>
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Attaching package: 'data.table'</code></pre>
<pre><code>The following objects are masked from 'package:dplyr':
between, first, last</code></pre>
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<pre><code>
Attaching package: 'cowplot'</code></pre>
<pre><code>The following object is masked from 'package:ggplot2':
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<p>Permuted Results from APA:</p>
<pre class="r"><code>nuclearAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt", stringsAsFactors = F, header = T)
totalAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_transcript_permResBH.txt", stringsAsFactors = F, header=T) </code></pre>
<p>I want to use a buzz swarm plot to plot peak usage for some of the top QTLs. I can use the examples I gave Tony.</p>
<p>Nuclear:<br />
* <strong>peak305794, sid: 7:128635754</strong></p>
<ul>
<li><strong>peak: 164036, sid: 2:3502035</strong></li>
</ul>
<p>Total:</p>
<ul>
<li><p><strong>Peak: peak228606, SID 3:150302010</strong></p></li>
<li><p>Peak: peak152751, SID 19:4236475</p></li>
</ul>
<p>I need to pull out the genotypes for each snp and the corresponding phenotype. I want to make a python script that I can give a snp and a peak and it will make a table with the genotypes and phenotypes for the necessary gene snp pair.</p>
</div>
<div id="example-peak-peak228606-sid-3150302010" class="section level2">
<h2>Example Peak: peak228606, SID 3:150302010</h2>
<pre class="r"><code>geno3_m=fread("../data/apaExamp/geno3_150302010.txt", header = T) %>% dplyr::select(starts_with("NA")) %>% t
geno3df= data.frame(geno3_m) %>% separate(geno3_m, into=c("geno", "dose", "extra"), sep=":") %>% dplyr::select(dose) %>% rownames_to_column(var="ind")
apaphen228606_m= fread("../data/apaExamp/Total.peak228606.txt", header = T) %>% dplyr::select(starts_with("NA")) %>% t
apaphen228606_df=data.frame(apaphen228606_m) %>% rownames_to_column(var="ind")</code></pre>
<pre class="r"><code>toplotAPA=geno3df %>% inner_join(apaphen228606_df, by="ind")
toplotAPA$dose= as.factor(toplotAPA$dose)
colnames(toplotAPA)= c("ind", "Genotype", "APA")
EIF2A_APAex=ggplot(toplotAPA, aes(y=APA, x=Genotype, by=Genotype, fill=Genotype)) + geom_boxplot() + geom_jitter() + labs(y="APA phenotype", title="Total APA: Peak 228606, EIF2A") + scale_fill_brewer(palette="YlOrRd")
ggsave("../output/plots/EIF2a_APA.png", EIF2A_APAex)</code></pre>
<pre><code>Saving 7 x 5 in image</code></pre>
<p>This is in the gene EIF2A, I need to find this in the eQTL data. The ensg id for this gene is ENSG00000144895.</p>
<pre class="r"><code>RNAseqEIF2A_m=read.table("../data/apaExamp/RNAseq.phenoEIF2A.txt", header=T) %>% dplyr::select(starts_with("NA")) %>% t
RNAseqEIF2A_df= data.frame(RNAseqEIF2A_m) %>% rownames_to_column("ind")
plotRNA=geno3df %>% inner_join(RNAseqEIF2A_df, by="ind")
plotRNA$dose= as.factor(plotRNA$dose)
colnames(plotRNA)= c("ind", "Genotype", "Expression")
EIF2A_RNAex=ggplot(plotRNA, aes(y=Expression, x=Genotype, by=Genotype, fill=Genotype)) + geom_boxplot() + geom_jitter() + labs(y="Normalized Expression", title="Gene Expression: EIF2A") + scale_fill_brewer(palette="YlGn")
ggsave("../output/plots/EIF2a_RNA.png", EIF2A_RNAex)</code></pre>
<pre><code>Saving 7 x 5 in image</code></pre>
<p>Try this in protein:</p>
<pre class="r"><code>ProtEIF2A_m=read.table("../data/apaExamp/ProtEIF2A.txt", header=T) %>% dplyr::select(starts_with("NA")) %>% t
ProtEIF2A_df= data.frame(ProtEIF2A_m) %>% rownames_to_column("ind")
plotProt=geno3df %>% inner_join(ProtEIF2A_df, by="ind")
plotProt$dose= as.factor(plotProt$dose)
colnames(plotProt)= c("ind", "Genotype", "Prot_level")
IF2A_Protex= ggplot(plotProt, aes(y=Prot_level, x=Genotype, by=Genotype, fill=Genotype)) + geom_boxplot() + geom_jitter() + labs(y="Normalized Protein Level", title="Protein Level: EIF2A") +scale_fill_brewer(palette="PuBu")
ggsave("../output/plots/EIF2a_Prot.png", IF2A_Protex)</code></pre>
<pre><code>Saving 7 x 5 in image</code></pre>
<pre class="r"><code>multphenoEIF2a=plot_grid(EIF2A_APAex,IF2A_Protex,EIF2A_RNAex,nrow=1)
ggsave("../output/plots/EIF2a_multpheno.png", multphenoEIF2a, width=15, height=5)</code></pre>
<p>Do this with 4su 60:</p>
<p>have to remove the #</p>
<pre class="r"><code>su60_EIF2A_m=read.table("../data/apaExamp/Foursu60EIF2A.txt", header=T) %>% dplyr::select(starts_with("NA")) %>% t
su60_EIF2A_df= data.frame(su60_EIF2A_m) %>% rownames_to_column("ind")
plot4su60=geno3df %>% inner_join(su60_EIF2A_df, by="ind")
plot4su60$dose= as.factor(plot4su60$dose)
colnames(plot4su60)= c("ind", "Genotype", "su60")
EIF2A_4su60ex=ggplot(plot4su60, aes(y=su60, x=Genotype, by=Genotype, fill=Genotype)) + geom_boxplot() + geom_jitter() + labs(y="4su60", title="4su 60min Value: EIF2A") + scale_fill_brewer(palette="RdPu") + theme_classic()
ggsave("../output/plots/EIF2a_4su60.png", EIF2A_4su60ex)</code></pre>
<pre><code>Saving 7 x 5 in image</code></pre>
<p>Geuvadis RNA</p>
<pre class="r"><code>rnaG_EIF2A_m=read.table("../data/apaExamp/RNA_GEU_EIF2A.txt", header=T) %>% dplyr::select(starts_with("NA")) %>% t
rnaG_EIF2A_df= data.frame(rnaG_EIF2A_m) %>% rownames_to_column("ind")
plotRNAg=geno3df %>% inner_join(rnaG_EIF2A_df, by="ind")
plotRNAg$dose= as.factor(plotRNAg$dose)
colnames(plotRNAg)= c("ind", "Genotype", "RNAg")
EIF2A_RNAgex=ggplot(plotRNAg, aes(y=RNAg, x=Genotype, by=Genotype, fill=Genotype)) + geom_boxplot() + geom_jitter() + labs(y="RNA expression", title="RNA Expression Geuvadis: EIF2A") + scale_fill_brewer(palette="RdPu")
ggsave("../output/plots/EIF2a_RNAg.png", EIF2A_RNAgex)</code></pre>
<pre><code>Saving 7 x 5 in image</code></pre>
<p>Ribo:</p>
<pre class="r"><code>ribo_EIF2A_m=read.table("../data/apaExamp/Ribo_EIF2A.txt", header=T) %>% dplyr::select(starts_with("NA")) %>% t
ribo_EIF2A_df= data.frame(ribo_EIF2A_m) %>% rownames_to_column("ind")
plotrib=geno3df %>% inner_join(ribo_EIF2A_df, by="ind")
plotrib$dose= as.factor(plotrib$dose)
colnames(plotrib)= c("ind", "Genotype", "Ribo")
EIF2A_riboex=ggplot(plotrib, aes(y=Ribo, x=Genotype, by=Genotype, fill=Genotype)) + geom_boxplot() + geom_jitter() + labs(y="RNA expression", title="Ribo Geuvadis: EIF2A") + scale_fill_brewer(palette="RdPu")
ggsave("../output/plots/EIF2a_Ribo.png", EIF2A_riboex)</code></pre>
<pre><code>Saving 7 x 5 in image</code></pre>
</div>
<div id="create-a-script-to-make-the-relevent-files" class="section level2">
<h2>Create a script to make the relevent files</h2>
<p>Python script that take a chromosome, snp, peak#, fraction</p>
<p>createQTLsnpAPAPhenTable.py</p>
<pre class="bash"><code>def main(PhenFile, GenFile, outFile, snp, peak):
fout=open(outFile, "w")
#Phen=open(PhenFile, "r")
Gen=open(GenFile, "r")
#get ind and pheno info
def get_pheno():
Phen=open(PhenFile, "r")
for num, ln in enumerate(Phen):
if num == 0:
indiv= ln.split()[4:]
else:
id=ln.split()[3].split(":")[3]
peakID=id.split("_")[2]
if peakID == peak:
pheno_list=ln.split()[4:]
pheno_data=list(zip(indiv,pheno_list))
print(pheno_data)
return(pheno_data)
def get_geno():
for num, lnG in enumerate(Gen):
if num == 13:
Ind_geno=lnG.split()[9:]
if num >= 14:
sid= lnG.split()[2]
if sid == snp:
gen_list=lnG.split()[9:]
allele1=[]
allele2=[]
for i in gen_list:
genotype=i.split(":")[0]
allele1.append(genotype.split("|")[0])
allele2.append(genotype.split("|")[1])
#now i have my indiv., phen, allele 1, alle 2
geno_data=list(zip(Ind_geno, allele1, allele2))
print(geno_data)
return(geno_data)
phenotype=get_pheno()
pheno_df=pd.DataFrame(data=phenotype,columns=["Ind", "Pheno"])
genotype=get_geno()
geno_df=pd.DataFrame(data=genotype, columns=["Ind", "Allele1", "Allele2"])
full_df=pd.merge(geno_df, pheno_df, how="inner", on="Ind")
full_df.to_csv(fout, sep="\t", encoding='utf-8', index=False)
fout.close()
if __name__ == "__main__":
import sys
import pandas as pd
chrom=sys.argv[1]
snp = sys.argv[2]
peak = sys.argv[3]
fraction=sys.argv[4]
PhenFile = "/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.%s.pheno_fixed.txt.gz.phen_chr%s"%(fraction, chrom)
GenFile= "/project2/gilad/briana/YRI_geno_hg19/chr%s.dose.filt.vcf"%(chrom)
outFile = "/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/qtlSNP_PeakAPA%s.%s%s.txt"%(fraction, snp, peak)
main(PhenFile, GenFile, outFile, snp, peak)
</code></pre>
<p>Use the results to plot the nuclear pheno:</p>
<pre class="r"><code>EIF2a_APAnuc=read.table("../data/apaExamp/qtlSNP_PeakAPANuclear.3:150302010peak228606.txt", header=T, stringsAsFactors = F) %>% mutate(Geno=Allele1 + Allele2)
EIF2a_APAnuc$Geno= as.factor(as.character(EIF2a_APAnuc$Geno))
ggplot(EIF2a_APAnuc, aes(y=Pheno, x=Geno, by=Geno, fill=Geno)) + geom_boxplot() + geom_jitter() + labs(y="APA Nuc Usage", title="APA nuc: EIF2A") + scale_fill_brewer(palette="RdPu")</code></pre>
<p><img src="figure/swarmPlots_QTLs.Rmd/unnamed-chunk-12-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-12-1.png:</em></summary>
<table style="border-collapse:separate; border-spacing:5px;">
<thead>
<tr>
<th style="text-align:left;">
Version
</th>
<th style="text-align:left;">
Author
</th>
<th style="text-align:left;">
Date
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/e73be701f5c8832eb0aaec4c5a4d0fe3bb508b0a/docs/figure/swarmPlots_QTLs.Rmd/unnamed-chunk-12-1.png" target="_blank">e73be70</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-10-09
</td>
</tr>
</tbody>
</table>
<p></details></p>
<p>This does the total and nuclear fraction of APA. I will do this for a snp and gene and get all of the other phenotypes. This will be similar other than changing the names of the genes and seperating the name for all but protein.</p>
<p>createQTLsnpMolPhenTable.py</p>
<pre class="bash"><code>def main(PhenFile, GenFile, outFile, snp, gene, molPhen):
#genenames=open("/project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt", "r" )
#for ln in genenames:
# geneName=ln.split()[1]
# if geneName == gene:
#gene_ensg=ln.split()[0]
gene_ensg=gene
fout=open(outFile, "w")
#Phen=open(PhenFile, "r")
Gen=open(GenFile, "r")
def getPheno(geneE=gene_ensg , mp=molPhen):
pheno=open(PhenFile, "r")
#get ind and pheno info
mp_use=mp[1:-1]
if mp_use=="prot":
for num,ln in enumerate(pheno):
if num == 0:
indiv= ln.split()[4:]
else:
gene=ln.split()[3]
if gene == str(geneE):
print("x")
pheno_list=ln.split()[4:]
pheno_data= list(zip(indiv, pheno_list))
return(pheno_data)
else:
for num,ln in enumerate(pheno):
if num == 0:
indiv= ln.split()[4:]
else:
full_gene=ln.split()[3]
gene= full_gene.split(".")[0]
if gene == geneE:
print(gene)
pheno_list=ln.split()[4:]
pheno_data= list(zip(indiv, pheno_list))
return(pheno_data)
def getGeno(geno, SNP):
for num, lnG in enumerate(geno):
if num == 13:
Ind_geno=lnG.split()[9:]
if num >= 14:
sid= lnG.split()[2]
if sid == SNP:
gen_list=lnG.split()[9:]
allele1=[]
allele2=[]
for i in gen_list:
genotype=i.split(":")[0]
allele1.append(genotype.split("|")[0])
allele2.append(genotype.split("|")[1])
#now i have my indiv., phen, allele 1, alle 2
geno_data=list(zip(Ind_geno, allele1, allele2))
return(geno_data)
phenotype_data=getPheno()
print(phenotype_data)
pheno_df=pd.DataFrame(data=phenotype_data,columns=["Ind", "Pheno"])
genotype_data=getGeno(Gen, snp)
print(genotype_data)
geno_df=pd.DataFrame(data=genotype_data, columns=["Ind", "Allele1", "Allele2"])
full_df=pd.merge(geno_df, pheno_df, how="inner", on="Ind")
full_df.to_csv(fout, sep="\t", encoding='utf-8', index=False)
fout.close()
if __name__ == "__main__":
import sys
import pandas as pd
chrom=sys.argv[1]
snp = sys.argv[2]
gene = sys.argv[3]
molPhen=sys.argv[4]
PhenFile = "/project2/gilad/briana/threeprimeseq/data/molecular_phenos/fastqtl_qqnorm%sphase2.fixed.noChr.txt"%(molPhen)
GenFile= "/project2/gilad/briana/YRI_geno_hg19/chr%s.dose.filt.vcf"%(chrom)
outFile = "/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/qtlSNP_Peak%s%s%s.txt"%(molPhen, snp, gene)
main(PhenFile, GenFile, outFile, snp, gene,molPhen)
</code></pre>
<p>test this:</p>
<pre class="bash"><code>python createQTLsnpMolPhenTable.py 3 3:150302010 EIF2A _RNAseq_</code></pre>
<p>list for phenos:</p>
<ul>
<li><p>4su_30</p></li>
<li><p>4su_60</p></li>
<li><p>RNAseqGeuvadis</p></li>
<li><p>RNAseq</p></li>
<li><p>prot</p></li>
<li><p>ribo</p></li>
</ul>
<p>Create a bash script that will use a for loop to run the python script on a all of the phenotypes</p>
<p>run_createQTLsnpMolPhenTable.sh</p>
<pre class="bash"><code>#!/bin/bash
#SBATCH --job-name=run_createQTLsnpMolPhenTable
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_createQTLsnpMolPhenTable.out
#SBATCH --error=run_createQTLsnpMolPhenTable.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load python
chrom=$1
snp=$2
gene=$3
for i in "_4su_30_" "_4su_60_" "_RNAseqGeuvadis_" "_RNAseq_" "_prot." "_ribo_"
do
python createQTLsnpMolPhenTable.py ${chrom} ${snp} ${gene} ${i}
done
</code></pre>
</div>
<div id="function-to-create-plots" class="section level2">
<h2>Function to create plots:</h2>
<p>I want to imput the files with the following structure:</p>
<p>/Users/bmittleman1/Documents/Gilad_lab/threeprimeseq/data/apaExamp/qtlSNP_Peak<em>molpheno</em>.snp.peak/gene.txt</p>
<p>I will use these to make cowplot with ggplot boxplots for each phenotypes. To do this I will create a function that takes in a snp, peak, and gene and creates each phenotype plot. It will then return the cowplot plot grid.</p>
<pre class="r"><code>plotQTL_func= function(SNP, peak, gene){
apaN_file=read.table(paste("../data/apaExamp/qtlSNP_PeakAPANuclear.", SNP, peak, ".txt", sep = "" ), header=T)
apaT_file=read.table(paste("../data/apaExamp/qtlSNP_PeakAPATotal.", SNP, peak, ".txt", sep = "" ), header=T)
su30_file=read.table(paste("../data/apaExamp/qtlSNP_Peak_4su_30_", SNP, gene, ".txt", sep=""), header = T)
su60_file=read.table(paste("../data/apaExamp/qtlSNP_Peak_4su_60_", SNP, gene, ".txt", sep=""), header=T)
RNA_file=read.table(paste("../data/apaExamp/qtlSNP_Peak_RNAseq_", SNP, gene, ".txt", sep=""),header=T)
RNAg_file=read.table(paste("../data/apaExamp/qtlSNP_Peak_RNAseqGeuvadis_", SNP, gene, ".txt", sep=""), header = T)
ribo_file=read.table(paste("../data/apaExamp/qtlSNP_Peak_ribo_", SNP, gene, ".txt", sep=""),header=T)
prot_file=read.table(paste("../data/apaExamp/qtlSNP_Peak_prot.", SNP, gene, ".txt", sep=""), header=T)
ggplot_func= function(file, molPhen,GENE){
file = file %>% mutate(genotype=Allele1 + Allele2)
file$genotype= as.factor(as.character(file$genotype))
plot=ggplot(file, aes(y=Pheno, x=genotype, by=genotype, fill=genotype)) + geom_boxplot(width=.25) + geom_jitter() + labs(y="Phenotpye",title=paste(molPhen, GENE, sep=": ")) + scale_fill_brewer(palette="Paired")
return(plot)
}
apaNplot=ggplot_func(apaN_file, "Apa Nuclear", gene)
apaTplot=ggplot_func(apaT_file, "Apa Total", gene)
su30plot=ggplot_func(su30_file, "4su30",gene)
su60plot=ggplot_func(su60_file, "4su60",gene)
RNAplot=ggplot_func(RNA_file, "RNA Seq",gene)
RNAgPlot=ggplot_func(RNAg_file, "RNA Seq Geuvadis",gene)
riboPlot= ggplot_func(ribo_file, "Ribo Seq",gene)
protplot=ggplot_func(prot_file, "Protein",gene)
full_plot= plot_grid(apaNplot,apaTplot, su30plot, su60plot, RNAplot, RNAgPlot, riboPlot, protplot,nrow=2)
return (full_plot)
}</code></pre>
<p>Try this with the EIF2A QTL:</p>
<pre class="r"><code>eif2a_allplots=plotQTL_func(SNP="3:150302010", peak="peak228606", gene="EIF2A")
ggsave("../output/plots/EIF2A_allplots.png", eif2a_allplots, height=5, width=12)</code></pre>
</div>
<div id="try-with-another-snp" class="section level2">
<h2>Try with another snp:</h2>
<ul>
<li>peak164036, sid: 2:3502035</li>
</ul>
<p>Step 1: Figure out what gene the peak is in.</p>
<pre class="bash"><code>grep peak164036 /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt</code></pre>
<p>This peak is in ADI1</p>
<p>Step2: Get the total and nuclear APA values by genotype with createQTLsnpAPAPhenTable.py</p>
<pre class="bash"><code>
python createQTLsnpAPAPhenTable.py 2 2:3502035 peak164036 Total
python createQTLsnpAPAPhenTable.py 2 2:3502035 peak164036 Nuclear</code></pre>
<p>Step 3: Get the ensg gene name:</p>
<pre class="bash"><code>grep ADI1 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt</code></pre>
<p>Step 4: Run this on the other phenotypes with : run_createQTLsnpMolPhenTable.sh</p>
<pre class="bash"><code>
sbatch run_createQTLsnpMolPhenTable.sh "2" "2:3502035" "ENSG00000182551"
</code></pre>
<p>Step 4: copy files to computer:</p>
<pre class="bash"><code>scp brimittleman@midway2.rcc.uchicago.edu:/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/qtlSNP_Peak*2:* . </code></pre>
<p>Step 5: plot</p>
<pre class="r"><code>plotQTL_func(SNP="2:3502035", peak="peak164036", gene="ENSG00000182551")</code></pre>
<p><img src="figure/swarmPlots_QTLs.Rmd/unnamed-chunk-23-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-23-1.png:</em></summary>
<table style="border-collapse:separate; border-spacing:5px;">
<thead>
<tr>
<th style="text-align:left;">
Version
</th>
<th style="text-align:left;">
Author
</th>
<th style="text-align:left;">
Date
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/8211a07a3567d273d979b7fc94d4f362d6e217b4/docs/figure/swarmPlots_QTLs.Rmd/unnamed-chunk-23-1.png" target="_blank">8211a07</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-10-11
</td>
</tr>
</tbody>
</table>
<p></details></p>
<ul>
<li>peak305794, sid: 7:128635754</li>
</ul>
<pre class="bash"><code>grep peak305794 /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt
#gene=IRF5
grep IRF5 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ensg= ENSG00000128604
python createQTLsnpAPAPhenTable.py 7 7:128635754 peak305794 Total
python createQTLsnpAPAPhenTable.py 7 7:128635754 peak305794 Nuclear
sbatch run_createQTLsnpMolPhenTable.sh "7" "7:128635754" "ENSG00000128604"
scp brimittleman@midway2.rcc.uchicago.edu:/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/qtlSNP_Peak_*7:* .
scp brimittleman@midway2.rcc.uchicago.edu:/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/qtlSNP_PeakAPA*.7* .</code></pre>
<pre class="r"><code>plotQTL_func(SNP="7:128635754", peak="peak305794", gene="ENSG00000128604")</code></pre>
<p><img src="figure/swarmPlots_QTLs.Rmd/unnamed-chunk-25-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-25-1.png:</em></summary>
<table style="border-collapse:separate; border-spacing:5px;">
<thead>
<tr>
<th style="text-align:left;">
Version
</th>
<th style="text-align:left;">
Author
</th>
<th style="text-align:left;">
Date
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/8211a07a3567d273d979b7fc94d4f362d6e217b4/docs/figure/swarmPlots_QTLs.Rmd/unnamed-chunk-25-1.png" target="_blank">8211a07</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-10-11
</td>
</tr>
</tbody>
</table>
<p></details></p>
<ul>
<li>Peak: peak152751, SID 19:4236475</li>
</ul>
<pre class="bash"><code>grep peak152751 /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt
#gene=EBI3
grep EBI3 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ensg= ENSG00000105246
python createQTLsnpAPAPhenTable.py 19 19:4236475 peak152751 Total
python createQTLsnpAPAPhenTable.py 19 19:4236475 peak152751 Nuclear
sbatch run_createQTLsnpMolPhenTable.sh "19" "19:4236475 " "ENSG00000105246"
scp brimittleman@midway2.rcc.uchicago.edu:/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/*19:4236475* .</code></pre>
<pre class="r"><code>plotQTL_func(SNP="19:4236475", peak="peak152751", gene="ENSG00000105246")</code></pre>
<p><img src="figure/swarmPlots_QTLs.Rmd/unnamed-chunk-27-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-27-1.png:</em></summary>
<table style="border-collapse:separate; border-spacing:5px;">
<thead>
<tr>
<th style="text-align:left;">
Version
</th>
<th style="text-align:left;">
Author
</th>
<th style="text-align:left;">
Date
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/8211a07a3567d273d979b7fc94d4f362d6e217b4/docs/figure/swarmPlots_QTLs.Rmd/unnamed-chunk-27-1.png" target="_blank">8211a07</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-10-11
</td>
</tr>
</tbody>
</table>
<p></details></p>
<ul>
<li>4:84382181:84382182:MRPS18C_+_peak241853, snp4:84382264</li>
</ul>
<pre class="bash"><code>
#gene=MRPS18C
grep MRPS18C /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ensg= ENSG00000163319
python createQTLsnpAPAPhenTable.py 4 4:84382264 peak241853 Total
python createQTLsnpAPAPhenTable.py 4 4:84382264 peak241853 Nuclear
sbatch run_createQTLsnpMolPhenTable.sh "4" "4:84382264 " "ENSG00000163319"
scp brimittleman@midway2.rcc.uchicago.edu:/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/*4:84382264* .</code></pre>
<p>We dont have protein information for this gene</p>
<pre class="r"><code>plotQTL_func(SNP="4:84382264", peak="peak241853", gene="ENSG00000163319")</code></pre>
<p><img src="figure/swarmPlots_QTLs.Rmd/unnamed-chunk-29-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-29-1.png:</em></summary>
<table style="border-collapse:separate; border-spacing:5px;">
<thead>
<tr>
<th style="text-align:left;">
Version
</th>
<th style="text-align:left;">
Author
</th>
<th style="text-align:left;">
Date
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/8211a07a3567d273d979b7fc94d4f362d6e217b4/docs/figure/swarmPlots_QTLs.Rmd/unnamed-chunk-29-1.png" target="_blank">8211a07</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-10-11
</td>
</tr>
</tbody>
</table>
<p></details></p>
<ul>
<li>7:66703515:66703516:TYW1_+_peak298097 7:66595366</li>
</ul>
<pre class="bash"><code>
#gene=TYW1
grep TYW1 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ensg= ENSG00000198874
python createQTLsnpAPAPhenTable.py 7 7:66595366 peak298097 Total
python createQTLsnpAPAPhenTable.py 7 7:66595366 peak298097 Nuclear
sbatch run_createQTLsnpMolPhenTable.sh "7" "7:66595366" "ENSG00000198874"
scp brimittleman@midway2.rcc.uchicago.edu:/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/*7:66595366* .</code></pre>
<pre class="r"><code>plotQTL_func(SNP="7:66595366", peak="peak298097", gene="ENSG00000198874")</code></pre>
<p><img src="figure/swarmPlots_QTLs.Rmd/unnamed-chunk-31-1.png" width="672" style="display: block; margin: auto;" /></p>
<ul>
<li>8:2792874:2792875:CSMD1_-_peak310334 8:3037787</li>
</ul>
<pre class="bash"><code>
#gene=CSMD1
grep CSMD1 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ensg= ENSG00000183117
python createQTLsnpAPAPhenTable.py 8 8:3037787 peak310334 Total
python createQTLsnpAPAPhenTable.py 8 8:3037787 peak310334 Nuclear
sbatch run_createQTLsnpMolPhenTable.sh "8" "8:3037787" "ENSG00000183117"
scp brimittleman@midway2.rcc.uchicago.edu:/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/*8:3037787* .</code></pre>
<p>We do not have molecular phenotypes for this gene.</p>
<ul>
<li>6:11183530:11183531:NEDD9_-_peak272002 6:11212754</li>
</ul>
<pre class="bash"><code>
#gene=NEDD9
grep NEDD9 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ensg= ENSG00000111859
python createQTLsnpAPAPhenTable.py 6 6:11212754 peak272002 Total
python createQTLsnpAPAPhenTable.py 6 6:11212754 peak272002 Nuclear
sbatch run_createQTLsnpMolPhenTable.sh "6" "6:11212754" "ENSG00000111859"
scp brimittleman@midway2.rcc.uchicago.edu:/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/*6:11212754* .</code></pre>
<pre class="r"><code>plotQTL_func(SNP="6:11212754", peak="peak272002", gene="ENSG00000111859")</code></pre>
<pre><code>Warning: Removed 9 rows containing non-finite values (stat_boxplot).</code></pre>
<pre><code>Warning: Removed 9 rows containing missing values (geom_point).</code></pre>
<p><img src="figure/swarmPlots_QTLs.Rmd/unnamed-chunk-34-1.png" width="672" style="display: block; margin: auto;" /></p>
<ul>
<li>12:51453213:51453214:LETMD1_+_peak71110 12:51405335</li>
</ul>
<pre class="bash"><code>
#gene=LETMD1
grep LETMD1 /project2/gilad/briana/genome_anotation_data/ensemble_to_genename.txt
#ensg= ENSG00000050426
python createQTLsnpAPAPhenTable.py 12 12:51405335 peak71110 Total
python createQTLsnpAPAPhenTable.py 12 12:51405335 peak71110 Nuclear
sbatch run_createQTLsnpMolPhenTable.sh "12" "12:51405335" "ENSG00000050426"
scp brimittleman@midway2.rcc.uchicago.edu:/project2/gilad/briana/threeprimeseq/data/ApaQTL_examples/*12:51405335* .</code></pre>
<pre class="r"><code>plotQTL_func(SNP="12:51405335", peak="peak71110", gene="ENSG00000050426")</code></pre>
<pre><code>Warning: Removed 7 rows containing non-finite values (stat_boxplot).</code></pre>
<pre><code>Warning: Removed 7 rows containing missing values (geom_point).</code></pre>
<p><img src="figure/swarmPlots_QTLs.Rmd/unnamed-chunk-36-1.png" width="672" style="display: block; margin: auto;" /></p>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>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] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] bindrcpp_0.2.2 cowplot_0.9.3 data.table_1.11.8
[4] VennDiagram_1.6.20 futile.logger_1.4.3 forcats_0.3.0
[7] stringr_1.3.1 dplyr_0.7.6 purrr_0.2.5
[10] readr_1.1.1 tidyr_0.8.1 tibble_1.4.2
[13] ggplot2_3.0.0 tidyverse_1.2.1 reshape2_1.4.3
[16] workflowr_1.1.1
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] RColorBrewer_1.1-2 lambda.r_1.2.3 modelr_0.1.2
[16] readxl_1.1.0 bindr_0.1.1 plyr_1.8.4
[19] munsell_0.5.0 gtable_0.2.0 cellranger_1.1.0
[22] rvest_0.3.2 R.methodsS3_1.7.1 evaluate_0.11
[25] labeling_0.3 knitr_1.20 broom_0.5.0
[28] Rcpp_0.12.19 formatR_1.5 backports_1.1.2
[31] scales_1.0.0 jsonlite_1.5 hms_0.4.2
[34] digest_0.6.17 stringi_1.2.4 rprojroot_1.3-2
[37] cli_1.0.1 tools_3.5.1 magrittr_1.5
[40] lazyeval_0.2.1 futile.options_1.0.1 crayon_1.3.4
[43] whisker_0.3-2 pkgconfig_2.0.2 xml2_1.2.0
[46] lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.10
[49] httr_1.3.1 rstudioapi_0.8 R6_2.3.0
[52] nlme_3.1-137 git2r_0.23.0 compiler_3.5.1 </code></pre>
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