<!DOCTYPE html>

<html xmlns="http://www.w3.org/1999/xhtml">

<head>

<meta charset="utf-8" />
<meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
<meta name="generator" content="pandoc" />


<meta name="author" content="Briana Mittleman" />


<title>peakOverlap_oppstrand</title>

<script src="site_libs/jquery-1.11.3/jquery.min.js"></script>
<meta name="viewport" content="width=device-width, initial-scale=1" />
<link href="site_libs/bootstrap-3.3.5/css/journal.min.css" rel="stylesheet" />
<script src="site_libs/bootstrap-3.3.5/js/bootstrap.min.js"></script>
<script src="site_libs/bootstrap-3.3.5/shim/html5shiv.min.js"></script>
<script src="site_libs/bootstrap-3.3.5/shim/respond.min.js"></script>
<script src="site_libs/jqueryui-1.11.4/jquery-ui.min.js"></script>
<link href="site_libs/tocify-1.9.1/jquery.tocify.css" rel="stylesheet" />
<script src="site_libs/tocify-1.9.1/jquery.tocify.js"></script>
<script src="site_libs/navigation-1.1/tabsets.js"></script>
<link href="site_libs/highlightjs-9.12.0/textmate.css" rel="stylesheet" />
<script src="site_libs/highlightjs-9.12.0/highlight.js"></script>
<link href="site_libs/font-awesome-5.0.13/css/fa-svg-with-js.css" rel="stylesheet" />
<script src="site_libs/font-awesome-5.0.13/js/fontawesome-all.min.js"></script>
<script src="site_libs/font-awesome-5.0.13/js/fa-v4-shims.min.js"></script>

<style type="text/css">code{white-space: pre;}</style>
<style type="text/css">
  pre:not([class]) {
    background-color: white;
  }
</style>
<script type="text/javascript">
if (window.hljs) {
  hljs.configure({languages: []});
  hljs.initHighlightingOnLoad();
  if (document.readyState && document.readyState === "complete") {
    window.setTimeout(function() { hljs.initHighlighting(); }, 0);
  }
}
</script>



<style type="text/css">
h1 {
  font-size: 34px;
}
h1.title {
  font-size: 38px;
}
h2 {
  font-size: 30px;
}
h3 {
  font-size: 24px;
}
h4 {
  font-size: 18px;
}
h5 {
  font-size: 16px;
}
h6 {
  font-size: 12px;
}
.table th:not([align]) {
  text-align: left;
}
</style>


</head>

<body>

<style type = "text/css">
.main-container {
  max-width: 940px;
  margin-left: auto;
  margin-right: auto;
}
code {
  color: inherit;
  background-color: rgba(0, 0, 0, 0.04);
}
img {
  max-width:100%;
  height: auto;
}
.tabbed-pane {
  padding-top: 12px;
}
.html-widget {
  margin-bottom: 20px;
}
button.code-folding-btn:focus {
  outline: none;
}
</style>


<style type="text/css">
/* padding for bootstrap navbar */
body {
  padding-top: 51px;
  padding-bottom: 40px;
}
/* offset scroll position for anchor links (for fixed navbar)  */
.section h1 {
  padding-top: 56px;
  margin-top: -56px;
}

.section h2 {
  padding-top: 56px;
  margin-top: -56px;
}
.section h3 {
  padding-top: 56px;
  margin-top: -56px;
}
.section h4 {
  padding-top: 56px;
  margin-top: -56px;
}
.section h5 {
  padding-top: 56px;
  margin-top: -56px;
}
.section h6 {
  padding-top: 56px;
  margin-top: -56px;
}
</style>

<script>
// manage active state of menu based on current page
$(document).ready(function () {
  // active menu anchor
  href = window.location.pathname
  href = href.substr(href.lastIndexOf('/') + 1)
  if (href === "")
    href = "index.html";
  var menuAnchor = $('a[href="' + href + '"]');

  // mark it active
  menuAnchor.parent().addClass('active');

  // if it's got a parent navbar menu mark it active as well
  menuAnchor.closest('li.dropdown').addClass('active');
});
</script>


<div class="container-fluid main-container">

<!-- tabsets -->
<script>
$(document).ready(function () {
  window.buildTabsets("TOC");
});
</script>

<!-- code folding -->




<script>
$(document).ready(function ()  {

    // move toc-ignore selectors from section div to header
    $('div.section.toc-ignore')
        .removeClass('toc-ignore')
        .children('h1,h2,h3,h4,h5').addClass('toc-ignore');

    // establish options
    var options = {
      selectors: "h1,h2,h3",
      theme: "bootstrap3",
      context: '.toc-content',
      hashGenerator: function (text) {
        return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase();
      },
      ignoreSelector: ".toc-ignore",
      scrollTo: 0
    };
    options.showAndHide = true;
    options.smoothScroll = true;

    // tocify
    var toc = $("#TOC").tocify(options).data("toc-tocify");
});
</script>

<style type="text/css">

#TOC {
  margin: 25px 0px 20px 0px;
}
@media (max-width: 768px) {
#TOC {
  position: relative;
  width: 100%;
}
}


.toc-content {
  padding-left: 30px;
  padding-right: 40px;
}

div.main-container {
  max-width: 1200px;
}

div.tocify {
  width: 20%;
  max-width: 260px;
  max-height: 85%;
}

@media (min-width: 768px) and (max-width: 991px) {
  div.tocify {
    width: 25%;
  }
}

@media (max-width: 767px) {
  div.tocify {
    width: 100%;
    max-width: none;
  }
}

.tocify ul, .tocify li {
  line-height: 20px;
}

.tocify-subheader .tocify-item {
  font-size: 0.90em;
  padding-left: 25px;
  text-indent: 0;
}

.tocify .list-group-item {
  border-radius: 0px;
}


</style>

<!-- setup 3col/9col grid for toc_float and main content  -->
<div class="row-fluid">
<div class="col-xs-12 col-sm-4 col-md-3">
<div id="TOC" class="tocify">
</div>
</div>

<div class="toc-content col-xs-12 col-sm-8 col-md-9">




<div class="navbar navbar-default  navbar-fixed-top" role="navigation">
  <div class="container">
    <div class="navbar-header">
      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar">
        <span class="icon-bar"></span>
        <span class="icon-bar"></span>
        <span class="icon-bar"></span>
      </button>
      <a class="navbar-brand" href="index.html">Three Prime Sequencing in Human LCLs</a>
    </div>
    <div id="navbar" class="navbar-collapse collapse">
      <ul class="nav navbar-nav">
        <li>
  <a href="index.html">Home</a>
</li>
<li>
  <a href="about.html">About</a>
</li>
<li>
  <a href="license.html">License</a>
</li>
      </ul>
      <ul class="nav navbar-nav navbar-right">
        <li>
  <a href="https://github.com/brimittleman/threeprimeseq">
    <span class="fa fa-github"></span>
     
  </a>
</li>
      </ul>
    </div><!--/.nav-collapse -->
  </div><!--/.container -->
</div><!--/.navbar -->

<!-- Add a small amount of space between sections. -->
<style type="text/css">
div.section {
  padding-top: 12px;
}
</style>

<div class="fluid-row" id="header">



<h1 class="title toc-ignore">peakOverlap_oppstrand</h1>
<h4 class="author"><em>Briana Mittleman</em></h4>
<h4 class="date"><em>8/30/2018</em></h4>

</div>


<p><strong>Last updated:</strong> 2018-09-05</p>
<strong>workflowr checks:</strong> <small>(Click a bullet for more information)</small>
<ul>
<li>
<p><details> <summary> <strong style="color:blue;">✔</strong> <strong>R Markdown file:</strong> up-to-date </summary></p>
<p>Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.</p>
</details>
</li>
<li>
<p><details> <summary> <strong style="color:blue;">✔</strong> <strong>Environment:</strong> empty </summary></p>
<p>Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.</p>
</details>
</li>
<li>
<p><details> <summary> <strong style="color:blue;">✔</strong> <strong>Seed:</strong> <code>set.seed(12345)</code> </summary></p>
<p>The command <code>set.seed(12345)</code> was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.</p>
</details>
</li>
<li>
<p><details> <summary> <strong style="color:blue;">✔</strong> <strong>Session information:</strong> recorded </summary></p>
<p>Great job! Recording the operating system, R version, and package versions is critical for reproducibility.</p>
</details>
</li>
<li>
<p><details> <summary> <strong style="color:blue;">✔</strong> <strong>Repository version:</strong> <a href="https://github.com/brimittleman/threeprimeseq/tree/008bb9ce009e02b40d1b6605e24df443d584c603" target="_blank">008bb9c</a> </summary></p>
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated. <br><br> Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use <code>wflow_publish</code> or <code>wflow_git_commit</code>). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
<pre><code>
Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    output/.DS_Store

Untracked files:
    Untracked:  analysis/ncbiRefSeq_sm.sort.mRNA.bed
    Untracked:  analysis/snake.config.notes.Rmd
    Untracked:  data/18486.genecov.txt
    Untracked:  data/APApeaksYL.total.inbrain.bed
    Untracked:  data/RNAkalisto/
    Untracked:  data/Totalpeaks_filtered_clean.bed
    Untracked:  data/YL-SP-18486-T-combined-genecov.txt
    Untracked:  data/YL-SP-18486-T_S9_R1_001-genecov.txt
    Untracked:  data/bedgraph_peaks/
    Untracked:  data/bin200.5.T.nuccov.bed
    Untracked:  data/bin200.Anuccov.bed
    Untracked:  data/bin200.nuccov.bed
    Untracked:  data/clean_peaks/
    Untracked:  data/comb_map_stats.csv
    Untracked:  data/comb_map_stats.xlsx
    Untracked:  data/combined_reads_mapped_three_prime_seq.csv
    Untracked:  data/gencov.test.csv
    Untracked:  data/gencov.test.txt
    Untracked:  data/gencov_zero.test.csv
    Untracked:  data/gencov_zero.test.txt
    Untracked:  data/gene_cov/
    Untracked:  data/joined
    Untracked:  data/leafcutter/
    Untracked:  data/merged_combined_YL-SP-threeprimeseq.bg
    Untracked:  data/nom_QTL/
    Untracked:  data/nom_QTL_opp/
    Untracked:  data/nuc6up/
    Untracked:  data/peakPerRefSeqGene/
    Untracked:  data/perm_QTL/
    Untracked:  data/perm_QTL_opp/
    Untracked:  data/reads_mapped_three_prime_seq.csv
    Untracked:  data/smash.cov.results.bed
    Untracked:  data/smash.cov.results.csv
    Untracked:  data/smash.cov.results.txt
    Untracked:  data/smash_testregion/
    Untracked:  data/ssFC200.cov.bed
    Untracked:  data/temp.file1
    Untracked:  data/temp.file2
    Untracked:  data/temp.gencov.test.txt
    Untracked:  data/temp.gencov_zero.test.txt
    Untracked:  output/picard/
    Untracked:  output/plots/
    Untracked:  output/qual.fig2.pdf

Unstaged changes:
    Modified:   analysis/28ind.peak.explore.Rmd
    Modified:   analysis/cleanupdtseq.internalpriming.Rmd
    Modified:   analysis/dif.iso.usage.leafcutter.Rmd
    Modified:   analysis/diff_iso_pipeline.Rmd
    Modified:   analysis/explore.filters.Rmd
    Modified:   analysis/peak.cov.pipeline.Rmd
    Modified:   analysis/pheno.leaf.comb.Rmd
    Modified:   analysis/test.max2.Rmd
    Modified:   code/Snakefile

</code></pre>
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes. </details>
</li>
</ul>
<details> <summary> <small><strong>Expand here to see past versions:</strong></small> </summary>
<ul>
<table style="border-collapse:separate; border-spacing:5px;">
<thead>
<tr>
<th style="text-align:left;">
File
</th>
<th style="text-align:left;">
Version
</th>
<th style="text-align:left;">
Author
</th>
<th style="text-align:left;">
Date
</th>
<th style="text-align:left;">
Message
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
Rmd
</td>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/008bb9ce009e02b40d1b6605e24df443d584c603/analysis/peakOverlap_oppstrand.Rmd" target="_blank">008bb9c</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-09-05
</td>
<td style="text-align:left;">
add QTL expamples
</td>
</tr>
<tr>
<td style="text-align:left;">
html
</td>
<td style="text-align:left;">
<a href="https://cdn.rawgit.com/brimittleman/threeprimeseq/4d70454fe14885375398c57551530f82ef8c366d/docs/peakOverlap_oppstrand.html" target="_blank">4d70454</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-08-31
</td>
<td style="text-align:left;">
Build site.
</td>
</tr>
<tr>
<td style="text-align:left;">
Rmd
</td>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/c4d943690857b4622955e1ba5648af513d34a3a2/analysis/peakOverlap_oppstrand.Rmd" target="_blank">c4d9436</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-08-31
</td>
<td style="text-align:left;">
add results and compare to ceu
</td>
</tr>
<tr>
<td style="text-align:left;">
html
</td>
<td style="text-align:left;">
<a href="https://cdn.rawgit.com/brimittleman/threeprimeseq/10c20cd9fae6b4433f499232ef22a1e0f0c24763/docs/peakOverlap_oppstrand.html" target="_blank">10c20cd</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-08-31
</td>
<td style="text-align:left;">
Build site.
</td>
</tr>
<tr>
<td style="text-align:left;">
Rmd
</td>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/1bc39533c1cce529df5f516bd1a90683aa2e2046/analysis/peakOverlap_oppstrand.Rmd" target="_blank">1bc3953</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-08-31
</td>
<td style="text-align:left;">
QTL results
</td>
</tr>
<tr>
<td style="text-align:left;">
html
</td>
<td style="text-align:left;">
<a href="https://cdn.rawgit.com/brimittleman/threeprimeseq/644eed717dbc3d1666374566ffdd55c1f286b166/docs/peakOverlap_oppstrand.html" target="_blank">644eed7</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-08-31
</td>
<td style="text-align:left;">
Build site.
</td>
</tr>
<tr>
<td style="text-align:left;">
Rmd
</td>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/3b9a50d76e0ad227c178f8f87bdbe1db3debee78/analysis/peakOverlap_oppstrand.Rmd" target="_blank">3b9a50d</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-08-31
</td>
<td style="text-align:left;">
add qtl code for normal cond.
</td>
</tr>
<tr>
<td style="text-align:left;">
html
</td>
<td style="text-align:left;">
<a href="https://cdn.rawgit.com/brimittleman/threeprimeseq/3834ae3e5d743c74ee429603825e282f3a397bea/docs/peakOverlap_oppstrand.html" target="_blank">3834ae3</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-08-30
</td>
<td style="text-align:left;">
Build site.
</td>
</tr>
<tr>
<td style="text-align:left;">
Rmd
</td>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/8e09d268908c553c5e645bb601551cdc3d91ff4d/analysis/peakOverlap_oppstrand.Rmd" target="_blank">8e09d26</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-08-30
</td>
<td style="text-align:left;">
feature counts -s2
</td>
</tr>
<tr>
<td style="text-align:left;">
html
</td>
<td style="text-align:left;">
<a href="https://cdn.rawgit.com/brimittleman/threeprimeseq/b4bda1b4c6cb9cd811f4133bca3184c00583c706/docs/peakOverlap_oppstrand.html" target="_blank">b4bda1b</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-08-30
</td>
<td style="text-align:left;">
Build site.
</td>
</tr>
<tr>
<td style="text-align:left;">
Rmd
</td>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/ed1f6582741ccdb98d00640a2d93ca8000fc2365/analysis/peakOverlap_oppstrand.Rmd" target="_blank">ed1f658</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-08-30
</td>
<td style="text-align:left;">
write code to rerun QTL with off strand
</td>
</tr>
</tbody>
</table>
</ul>
<p></details></p>
<hr />
<p>In the dataprocfigures file I realized the peaks mapp to the opposite strand. I want to remap the peaks to genes on the opposite strand make the phenotpyes and rerun the QTL analysis.</p>
<div id="map-peaks-to-genes-on-opp-strand" class="section level2">
<h2>Map peaks to genes on opp strand</h2>
<pre class="bash"><code>#!/bin/bash

#SBATCH --job-name=intGenes_combfilterPeaksOppStrand
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=intGenes_combfilterPeaksOppStrand.out
#SBATCH --error=intGenes_combfilterPeaksOppStrand.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

bedtools intersect -wa -wb -sorted -S -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed -b /project2/gilad/briana/genome_anotation_data/ncbiRefSeq_sm_noChr.sort.mRNA.bed &gt; /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand.bed</code></pre>
<p>Get rid of the extra columns. I will now use the gene strand so the feature counts can be stranded.</p>
<pre class="bash"><code>awk &#39;{print $1 &quot;\t&quot; $2 &quot;\t&quot; $3 &quot;\t&quot; $4 &quot;\t&quot; $5 &quot;\t&quot; $12 &quot;\t&quot; $10}&#39; /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand.bed &gt; /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm.bed</code></pre>
<p>Make this an SAF file with the correct peak ID. bed2saf_oppstrand_peaks.py</p>
<pre class="bash"><code>from misc_helper import *

fout = file(&quot;/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm.SAF&quot;,&#39;w&#39;)
fout.write(&quot;GeneID\tChr\tStart\tEnd\tStrand\n&quot;)
for ln in open(&quot;/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm.bed&quot;):
    chrom, start, end, name, score, strand, gene = ln.split()
    name_i=int(name)
    start_i=int(start)
    end_i=int(end)
    ID = &quot;peak%d:%s:%d:%d:%s:%s&quot;%(name_i, chrom, start_i, end_i, strand, gene)
    fout.write(&quot;%s\t%s\t%d\t%d\t%s\n&quot;%(ID, chrom, start_i, end_i, strand))
fout.close()</code></pre>
</div>
<div id="create-leafcutter-phenotypes" class="section level2">
<h2>Create leafcutter phenotypes</h2>
<p>Run feature counts:<br />
ref_gene_peakOppStrand_fc.sh</p>
<pre class="bash"><code>
#!/bin/bash

#SBATCH --job-name=ref_gene_peakOppStrand_fc
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=ref_gene_peakOppStrand_fc.out
#SBATCH --error=ref_gene_peakOppStrand_fc.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


featureCounts -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.fc /project2/gilad/briana/threeprimeseq/data/sort/*-sort.bam -s 2
</code></pre>
<p>Also do this for total and nuclear seperately.</p>
<pre class="bash"><code>#!/bin/bash

#SBATCH --job-name=ref_gene_peakOppStrand_fc_TN
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=ref_gene_peakOppStrand_fc_TN.out
#SBATCH --error=ref_gene_peakOppStrand_fc_TN.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

featureCounts -O -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total.fc /project2/gilad/briana/threeprimeseq/data/sort/*-T-*-sort.bam -s 2

featureCounts -O -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear.fc /project2/gilad/briana/threeprimeseq/data/sort/*-N-*-sort.bam -s 2</code></pre>
<p>Fix the headers:</p>
<ul>
<li>fix_head_fc_opp_tot.py</li>
</ul>
<pre class="bash"><code>infile= open(&quot;/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total.fc&quot;, &quot;r&quot;)
fout = file(&quot;/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total_fixed.fc&quot;,&#39;w&#39;)
for line, i in enumerate(infile):
    if line == 1:
        i_list=i.split()
        libraries=i_list[:6]
        for sample in i_list[6:]:
            full = sample.split(&quot;/&quot;)[7]
            samp= full.split(&quot;-&quot;)[2:4]
            lim=&quot;_&quot;
            samp_st=lim.join(samp)
            libraries.append(samp_st)
        first_line= &quot;\t&quot;.join(libraries)
        fout.write(first_line + &#39;\n&#39;)
    else :
        fout.write(i)
fout.close()</code></pre>
<ul>
<li>fix_head_fc_opp_nuc.py</li>
</ul>
<pre class="bash"><code>infile= open(&quot;/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear.fc&quot;, &quot;r&quot;)
fout = file(&quot;/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear_fixed.fc&quot;,&#39;w&#39;)
for line, i in enumerate(infile):
    if line == 1:
        i_list=i.split()
        libraries=i_list[:6]
        for sample in i_list[6:]:
            full = sample.split(&quot;/&quot;)[7]
            samp= full.split(&quot;-&quot;)[2:4]
            lim=&quot;_&quot;
            samp_st=lim.join(samp)
            libraries.append(samp_st)
        first_line= &quot;\t&quot;.join(libraries)
        fout.write(first_line + &#39;\n&#39;)
    else :
        fout.write(i)
fout.close()</code></pre>
<p>Create file IDS:</p>
<ul>
<li>create_fileid_opp_total.py</li>
</ul>
<pre class="bash"><code>fout = file(&quot;/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total.txt&quot;,&#39;w&#39;)
infile= open(&quot;/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total_fixed.fc&quot;, &quot;r&quot;)
for line, i in enumerate(infile):
    if line == 0:
        i_list=i.split()
        files= i_list[10:-2]
        for each in files:
            full = each.split(&quot;/&quot;)[7]
            samp= full.split(&quot;-&quot;)[2:4]
            lim=&quot;_&quot;
            samp_st=lim.join(samp)
            outLine= full[:-1] + &quot;\t&quot; + samp_st
            fout.write(outLine + &quot;\n&quot;)
fout.close()</code></pre>
<ul>
<li>create_fileid_opp_nuc.py</li>
</ul>
<pre class="bash"><code>fout = file(&quot;/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_nuclear.txt&quot;,&#39;w&#39;)
infile= open(&quot;/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear_fixed.fc&quot;, &quot;r&quot;)
for line, i in enumerate(infile):
    if line == 0:
        i_list=i.split()
        files= i_list[10:-2]
        for each in files:
            full = each.split(&quot;/&quot;)[7]
            samp= full.split(&quot;-&quot;)[2:4]
            lim=&quot;_&quot;
            samp_st=lim.join(samp)
            outLine= full[:-1] + &quot;\t&quot; + samp_st
            fout.write(outLine + &quot;\n&quot;)
fout.close()</code></pre>
<p>Make Phenotypes:</p>
<ul>
<li>makePhenoRefSeqPeaks_opp_Total.py</li>
</ul>
<pre class="bash"><code>#PYTHON 3

dic_IND = {}
dic_BAM = {}

for ln in open(&quot;/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total.txt&quot;):
    bam, IND = ln.split(&quot;\t&quot;)
    IND = IND.strip()
    dic_IND[bam] = IND
    if IND not in dic_BAM:
        dic_BAM[IND] = []
    dic_BAM[IND].append(bam)


#now I have ind dic with keys as the bam and ind as the values
#I also have a bam dic with ind as the keys and bam as the values  
    
inds=list(dic_BAM.keys()) #list of ind libraries  

#list of genes   

count_file=open(&quot;/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total_fixed.fc&quot;, &quot;r&quot;)
genes=[]
for line , i in enumerate(count_file):
    if line &gt; 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(&quot;:&quot;)
        gene=id_list[5]
        if gene not in genes:
            genes.append(gene)
            
#make the ind and gene dic  
dic_dub={}
for g in genes:
    dic_dub[g]={}
    for i in inds:
        dic_dub[g][i]=0


#populate the dictionary  
count_file=open(&quot;/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total_fixed.fc&quot;, &quot;r&quot;)
for line, i in enumerate(count_file):
    if line &gt; 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(&quot;:&quot;)
        g= id_list[5]
        values=list(i_list[6:])
        list_list=[]
        for ind,val in zip(inds, values):
            list_list.append([ind, val])
        for num, name in enumerate(list_list):
            dic_dub[g][list_list[num][0]] += int(list_list[num][1])
        

#write the file by acessing the dictionary and putting values in the table ver the value in the dic 
        

fout=open(&quot;/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total.pheno_fixed.txt&quot;,&quot;w&quot;)
peak=[&quot;chrom&quot;]
inds_noL=[]
for each in inds:
    indsNA= &quot;NA&quot; + each[:-2]
    inds_noL.append(indsNA) 
fout.write(&quot; &quot;.join(peak + inds_noL) + &#39;\n&#39; )


count_file=open(&quot;/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total_fixed.fc&quot;, &quot;r&quot;)
for line , i in enumerate(count_file):
    if line &gt; 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(&quot;:&quot;)
        gene=id_list[5]
        start=int(id_list[2])
        end=int(id_list[3])
        buff=[]
        buff.append(&quot;chr%s:%d:%d:%s_%s_%s&quot;%(id_list[1], start, end, id_list[5], id_list[4], id_list[0]))
        for x,y in zip(i_list[6:], inds):
            b=int(dic_dub[gene][y])
            t=int(x)
            buff.append(&quot;%d/%d&quot;%(t,b))
        fout.write(&quot; &quot;.join(buff)+ &#39;\n&#39;)
        
fout.close()</code></pre>
<ul>
<li>makePhenoRefSeqPeaks_opp_Nuclear.py</li>
</ul>
<pre class="bash"><code>#PYTHON 3

dic_IND = {}
dic_BAM = {}

for ln in open(&quot;/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_nuclear.txt&quot;):
    bam, IND = ln.split(&quot;\t&quot;)
    IND = IND.strip()
    dic_IND[bam] = IND
    if IND not in dic_BAM:
        dic_BAM[IND] = []
    dic_BAM[IND].append(bam)


#now I have ind dic with keys as the bam and ind as the values
#I also have a bam dic with ind as the keys and bam as the values  
    
inds=list(dic_BAM.keys()) #list of ind libraries  

#list of genes   

count_file=open(&quot;/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear_fixed.fc&quot;, &quot;r&quot;)
genes=[]
for line , i in enumerate(count_file):
    if line &gt; 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(&quot;:&quot;)
        gene=id_list[5]
        if gene not in genes:
            genes.append(gene)
            
#make the ind and gene dic  
dic_dub={}
for g in genes:
    dic_dub[g]={}
    for i in inds:
        dic_dub[g][i]=0


#populate the dictionary  
count_file=open(&quot;/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear_fixed.fc&quot;, &quot;r&quot;)
for line, i in enumerate(count_file):
    if line &gt; 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(&quot;:&quot;)
        g= id_list[5]
        values=list(i_list[6:])
        list_list=[]
        for ind,val in zip(inds, values):
            list_list.append([ind, val])
        for num, name in enumerate(list_list):
            dic_dub[g][list_list[num][0]] += int(list_list[num][1])
        

#write the file by acessing the dictionary and putting values in the table ver the value in the dic 
        

fout=open(&quot;/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear.pheno_fixed.txt&quot;,&quot;w&quot;)
peak=[&quot;chrom&quot;]
inds_noL=[]
for each in inds:
    indsNA= &quot;NA&quot; + each[:-2]
    inds_noL.append(indsNA)  
fout.write(&quot; &quot;.join(peak + inds_noL) + &#39;\n&#39; )
count_file=open(&quot;/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear_fixed.fc&quot;, &quot;r&quot;)
for line , i in enumerate(count_file):
    if line &gt; 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(&quot;:&quot;)
        gene=id_list[5]
        start=int(id_list[2])
        end=int(id_list[3])
        buff=[]
        buff.append(&quot;chr%s:%d:%d:%s_%s_%s&quot;%(id_list[1], start, end, id_list[5], id_list[4], id_list[0]))
        for x,y in zip(i_list[6:], inds):
            b=int(dic_dub[gene][y])
            t=int(x)
            buff.append(&quot;%d/%d&quot;%(t,b))
        fout.write(&quot; &quot;.join(buff)+ &#39;\n&#39;)
        
fout.close()</code></pre>
<p>I can run these with the following bash script:</p>
<ul>
<li>run_makePhen_sep_Opp.sh</li>
</ul>
<pre class="bash"><code>#!/bin/bash

#SBATCH --job-name=run_makepheno_sep
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_makepheno_sep.out
#SBATCH --error=run_makepheno_sep.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

python makePhenoRefSeqPeaks_opp_Total.py 

python makePhenoRefSeqPeaks_opp_Nuclear.py 
</code></pre>
</div>
<div id="prepare-for-fastqtl" class="section level2">
<h2>Prepare for FastQTL</h2>
<p>I will do this in the /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/ directory.</p>
<pre class="bash"><code>module load samtools
#zip file 
gzip filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total.pheno_fixed.txt

module load python
#leafcutter script
python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total.pheno_fixed.txt.gz 

#source activate three-prime-env
sh filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total.pheno_fixed.txt_prepare.sh

#run for nuclear as well 
gzip  filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear.pheno_fixed.txt
#unload anaconda, load python
python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear.pheno_fixed.txt.gz 
#load anaconda and env. 
sh filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear.pheno_fixed.txt.gz_prepare.sh



#keep only 2 PCs
#filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.PCs
#filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.PCs</code></pre>
<p>Make a sample list. ls</p>
<ul>
<li>makeSampleList_opp.py</li>
</ul>
<pre class="bash"><code>#make a sample list  

fout = open(&quot;/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/SAMPLE.txt&quot;,&#39;w&#39;)

for ln in open(&quot;/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_nuclear.txt&quot;, &quot;r&quot;):
    bam, sample = ln.split()
    line=sample[:-2]
    fout.write(&quot;NA&quot;+line + &quot;\n&quot;)
fout.close()</code></pre>
<p>** Manually ** Remove 18500, 19092 and 19193, 18497</p>
</div>
<div id="run-fastqtl" class="section level2">
<h2>Run FastQTL</h2>
<div id="nominal" class="section level3">
<h3>Nominal</h3>
<ul>
<li>APAqtl_nominal_oppstrand.sh</li>
</ul>
<pre class="bash"><code>#!/bin/bash


#SBATCH --job-name=APAqtl_nominal_opp
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_nominal_opp.out
#SBATCH --error=APAqtl_nominal_opp.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$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_Opp/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/phenotypes_filtPeakOppstrand/SAMPLE.txt
done


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$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_Opp/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/phenotypes_filtPeakOppstrand/SAMPLE.txt
done
</code></pre>
</div>
<div id="permuted" class="section level3">
<h3>Permuted</h3>
<ul>
<li>APAqtl_perm_Opp.sh</li>
</ul>
<pre class="bash"><code>#!/bin/bash


#SBATCH --job-name=APAqtl_perm_opp
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_perm_opp.out
#SBATCH --error=APAqtl_perm_opp.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$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/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/phenotypes_filtPeakOppstrand/SAMPLE.txt
done



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$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/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/phenotypes_filtPeakOppstrand/SAMPLE.txt
done
</code></pre>
<p>Make sure to create directory for out before running this.</p>
<p>Run normal version for:</p>
<ul>
<li>total 4/15</li>
<li>nuclear 4/15</li>
</ul>
<p>APAqtl_nominal_norm_opp.sh</p>
<pre class="bash"><code>#!/bin/bash


#SBATCH --job-name=APAqtl_nominal_norm_opp
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_nominal_norm_opp.out
#SBATCH --error=APAqtl_nominal__nrom_opp.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

for i in 4 15
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --normal --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_Opp/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/phenotypes_filtPeakOppstrand/SAMPLE.txt
done


for i in 4 15 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --normal --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_Opp/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/phenotypes_filtPeakOppstrand/SAMPLE.txt
done
</code></pre>
<p>Error in some of the permutations due to need for normal condition. Running these with :</p>
<p>APAqtl_perm_norm_opp.sh</p>
<pre class="bash"><code>#!/bin/bash


#SBATCH --job-name=APAqtl_perm_opp
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_perm_opp.out
#SBATCH --error=APAqtl_perm_opp.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END


#for i in  1 15 4  
#do
#/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --permute 1000 --normal --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/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/phenotypes_filtPeakOppstrand/SAMPLE.txt
#done



for i in 21
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --permute 1000 --normal --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakOppstrand/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/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/phenotypes_filtPeakOppstrand/SAMPLE.txt
done
</code></pre>
<p><a href="https://brimittleman.github.io/threeprimeseq/apaQTLwLeafcutter.html" class="uri">https://brimittleman.github.io/threeprimeseq/apaQTLwLeafcutter.html</a></p>
</div>
</div>
<div id="evaluate-permuted-results" class="section level2">
<h2>Evaluate Permuted results</h2>
<pre class="r"><code>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(workflowr)</code></pre>
<pre><code>This is workflowr version 1.1.1
Run ?workflowr for help getting started</code></pre>
<pre class="r"><code>library(cowplot)</code></pre>
<pre><code>
Attaching package: &#39;cowplot&#39;</code></pre>
<pre><code>The following object is masked from &#39;package:ggplot2&#39;:

    ggsave</code></pre>
<pre class="r"><code>library(reshape2)</code></pre>
<pre><code>
Attaching package: &#39;reshape2&#39;</code></pre>
<pre><code>The following object is masked from &#39;package:tidyr&#39;:

    smiths</code></pre>
<div id="total" class="section level3">
<h3>Total:</h3>
<pre class="r"><code>tot.perm= read.table(&quot;../data/perm_QTL_opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_permRes.txt&quot;,head=F, stringsAsFactors=F, col.names = c(&quot;pid&quot;, &quot;nvar&quot;, &quot;shape1&quot;, &quot;shape2&quot;, &quot;dummy&quot;, &quot;sid&quot;, &quot;dist&quot;, &quot;npval&quot;, &quot;slope&quot;, &quot;ppval&quot;, &quot;bpval&quot;))


plot(tot.perm$ppval, tot.perm$bpval, xlab=&quot;Direct method&quot;, ylab=&quot;Beta approximation&quot;, main=&quot;Total Check plot&quot;)
abline(0, 1, col=&quot;red&quot;)</code></pre>
<p><img src="figure/peakOverlap_oppstrand.Rmd/unnamed-chunk-20-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-20-1.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/10c20cd9fae6b4433f499232ef22a1e0f0c24763/docs/figure/peakOverlap_oppstrand.Rmd/unnamed-chunk-20-1.png" target="_blank">10c20cd</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-08-31
</td>
</tr>
</tbody>
</table>
<p></details></p>
<p>Correct with Benjamini Hochberg:</p>
<pre class="r"><code>tot.perm$bh=p.adjust(tot.perm$bpval, method=&quot;fdr&quot;)
plot(-log10(tot.perm$bh), main=&quot;Total BH corrected pval&quot;)
abline(h=1,col=&quot;Red&quot;)</code></pre>
<p><img src="figure/peakOverlap_oppstrand.Rmd/unnamed-chunk-21-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-21-1.png:</em></summary>
<table style="border-collapse:separate; border-spacing:5px;">
<thead>
<tr>
<th style="text-align:left;">
Version
</th>
<th style="text-align:left;">
Author
</th>
<th style="text-align:left;">
Date
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/10c20cd9fae6b4433f499232ef22a1e0f0c24763/docs/figure/peakOverlap_oppstrand.Rmd/unnamed-chunk-21-1.png" target="_blank">10c20cd</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-08-31
</td>
</tr>
</tbody>
</table>
<p></details></p>
<p>QQ plot:</p>
<pre class="r"><code>qqplot(-log10(runif(nrow(tot.perm))), -log10(tot.perm$bpval),ylab=&quot;-log10 Total permuted pvalue&quot;, xlab=&quot;Uniform expectation&quot;, main=&quot;Total permuted pvalues for all snps&quot;)
abline(0,1)</code></pre>
<p><img src="figure/peakOverlap_oppstrand.Rmd/unnamed-chunk-22-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-22-1.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/10c20cd9fae6b4433f499232ef22a1e0f0c24763/docs/figure/peakOverlap_oppstrand.Rmd/unnamed-chunk-22-1.png" target="_blank">10c20cd</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-08-31
</td>
</tr>
</tbody>
</table>
<p></details></p>
<pre class="r"><code>tot_qtl_10= tot.perm %&gt;% filter(-log10(bh) &gt; 1) %&gt;% nrow()
tot_qtl_10</code></pre>
<pre><code>[1] 4</code></pre>
</div>
<div id="nuclear" class="section level3">
<h3>Nuclear:</h3>
<pre class="r"><code>nuc.perm= read.table(&quot;../data/perm_QTL_opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_permRes.txt&quot;,head=F, stringsAsFactors=F, col.names = c(&quot;pid&quot;, &quot;nvar&quot;, &quot;shape1&quot;, &quot;shape2&quot;, &quot;dummy&quot;, &quot;sid&quot;, &quot;dist&quot;, &quot;npval&quot;, &quot;slope&quot;, &quot;ppval&quot;, &quot;bpval&quot;))


plot(nuc.perm$ppval, nuc.perm$bpval, xlab=&quot;Direct method&quot;, ylab=&quot;Beta approximation&quot;, main=&quot;Nuclear Check plot&quot;)
abline(0, 1, col=&quot;red&quot;)</code></pre>
<p><img src="figure/peakOverlap_oppstrand.Rmd/unnamed-chunk-24-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-24-1.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/10c20cd9fae6b4433f499232ef22a1e0f0c24763/docs/figure/peakOverlap_oppstrand.Rmd/unnamed-chunk-24-1.png" target="_blank">10c20cd</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-08-31
</td>
</tr>
</tbody>
</table>
<p></details></p>
<p>Correct with Benjamini Hochberg:</p>
<pre class="r"><code>nuc.perm$bh=p.adjust(nuc.perm$bpval, method=&quot;fdr&quot;)
plot(-log10(nuc.perm$bh), main=&quot;Nuclear BH corrected pval&quot;)
abline(h=1,col=&quot;Red&quot;)</code></pre>
<p><img src="figure/peakOverlap_oppstrand.Rmd/unnamed-chunk-25-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-25-1.png:</em></summary>
<table style="border-collapse:separate; border-spacing:5px;">
<thead>
<tr>
<th style="text-align:left;">
Version
</th>
<th style="text-align:left;">
Author
</th>
<th style="text-align:left;">
Date
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/10c20cd9fae6b4433f499232ef22a1e0f0c24763/docs/figure/peakOverlap_oppstrand.Rmd/unnamed-chunk-25-1.png" target="_blank">10c20cd</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-08-31
</td>
</tr>
</tbody>
</table>
<p></details></p>
<pre class="r"><code>nuc_qtl_10= nuc.perm %&gt;% filter(-log10(bh) &gt; 1) %&gt;% nrow()
nuc_qtl_10</code></pre>
<pre><code>[1] 522</code></pre>
<p>QQ plot:</p>
<pre class="r"><code>qqplot(-log10(runif(nrow(nuc.perm))), -log10(nuc.perm$bpval),ylab=&quot;-log10 Total permuted pvalue&quot;, xlab=&quot;Uniform expectation&quot;, main=&quot;Nuclear permuted pvalues for all snps&quot;)
abline(0,1)</code></pre>
<p><img src="figure/peakOverlap_oppstrand.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/10c20cd9fae6b4433f499232ef22a1e0f0c24763/docs/figure/peakOverlap_oppstrand.Rmd/unnamed-chunk-27-1.png" target="_blank">10c20cd</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-08-31
</td>
</tr>
</tbody>
</table>
<p></details></p>
</div>
<div id="compare-number" class="section level3">
<h3>Compare number</h3>
<pre class="r"><code>nQTL_tot=c()
FDR=seq(.05, .5, .01)
for (i in FDR){
  x=tot.perm %&gt;% filter(bh &lt; i ) %&gt;% nrow()
  nQTL_tot=c(nQTL_tot, x)
}

FDR=seq(.05, .5, .01)
nQTL_nuc=c()
for (i in FDR){
  x=nuc.perm %&gt;% filter(bh &lt; i ) %&gt;% 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 = &quot;FDR&quot;)

ggplot(nQTL_long, aes(x=FDR, y=value, by=variable, col=variable)) + geom_line(size=1.5) + labs(y=&quot;Number of Significant QTLs&quot;, title=&quot;APAqtls detected by FDR cuttoff&quot;, color=&quot;Fraction&quot;)</code></pre>
<p><img src="figure/peakOverlap_oppstrand.Rmd/unnamed-chunk-28-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-28-1.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/10c20cd9fae6b4433f499232ef22a1e0f0c24763/docs/figure/peakOverlap_oppstrand.Rmd/unnamed-chunk-28-1.png" target="_blank">10c20cd</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-08-31
</td>
</tr>
</tbody>
</table>
<p></details></p>
</div>
</div>
<div id="condition-on-qtls-from-ceu" class="section level2">
<h2>Condition on QTLs from CEU</h2>
<p>The nominal results is super big. I am going to sort it by pvalue and keep only 1 in 10.</p>
<pre class="bash"><code>sort -k 4 -n -r filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt | awk &#39;NR == 1 || NR % 10 == 0&#39; &gt; filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes_onetenth.txt</code></pre>
<pre class="r"><code>ceu_QTL=read.table(&quot;../data/nom_QTL/ceu.apaqtl.txt.gz.bh.txt&quot;, header = T, stringsAsFactors = F)
nuc.nom=read.table(&quot;../data/nom_QTL_opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes_onetenth.txt&quot;, stringsAsFactors = F)
colnames(nuc.nom)= c(&quot;peakID&quot;, &quot;snpID&quot;, &quot;dist&quot;, &quot;nuc_pval&quot;, &quot;slope&quot;)


ceu_QTL_snp=ceu_QTL %&gt;% filter(grepl(&quot;snp&quot;, dummy2)) %&gt;% separate(dummy2, c(&quot;type&quot;, &quot;chr&quot;, &quot;loc&quot;), sep=&quot;_&quot;) %&gt;% unite(snpID, c(&quot;chr&quot;, &quot;loc&quot;), sep=&quot;:&quot;)


ceuAndNuc= ceu_QTL_snp %&gt;% inner_join(nuc.nom, by=&quot;snpID&quot;) %&gt;% select(snpID, bpval, nuc_pval)
nuc_ceuSNPS=runif(nrow(ceuAndNuc))</code></pre>
<pre class="r"><code>#plot qqplot

qqplot(-log10(runif(nrow(nuc.nom))), -log10(nuc.nom$nuc_pval),ylab=&quot;-log10 Total nominal pvalue&quot;, xlab=&quot;Uniform expectation&quot;, main=&quot;Nuclear Nominal pvalues for all snps&quot;)
points(sort(-log10(nuc_ceuSNPS)), sort(-log10(ceuAndNuc$nuc_pval)), col=&quot;Red&quot;)
abline(0,1)
legend(&quot;topleft&quot;, legend=c(&quot;All SNPs&quot;, &quot;SNP in CEU APAqtls&quot;), col=c(&quot;black&quot;, &quot;red&quot;), pch=19)</code></pre>
<p><img src="figure/peakOverlap_oppstrand.Rmd/unnamed-chunk-31-1.png" width="672" style="display: block; margin: auto;" /> Unique snp QTLs</p>
<pre class="r"><code>nuc.perm %&gt;% filter(-log10(bh) &gt; 1) %&gt;%  summarise(n_distinct(sid)) </code></pre>
<pre><code>  n_distinct(sid)
1             204</code></pre>
</div>
<div id="plot-qtl-examples" class="section level2">
<h2>Plot QTL examples</h2>
<p>I will make boxplots of the most significant Qtls in the nuclear fraction. I can use the python script I created for <a href="https://brimittleman.github.io/threeprimeseq/apaQTLwLeafcutter.html" class="uri">https://brimittleman.github.io/threeprimeseq/apaQTLwLeafcutter.html</a> called filter_geno.py.</p>
<pre class="r"><code>nuc.perm %&gt;% filter(-log10(bh) &gt; 1) %&gt;% mutate(neglogBH=-log10(bh)) %&gt;% arrange(desc(neglogBH)) %&gt;% select(pid,sid, neglogBH) %&gt;% top_n(10) </code></pre>
<pre><code>Selecting by neglogBH</code></pre>
<pre><code>                                                pid         sid neglogBH
1  20:42274422:42274503:NM_001303459.2_+_peak206560 20:42285456 4.765160
2  20:42274422:42274503:NM_001323578.1_+_peak206560 20:42285456 4.765160
3  20:42274422:42274503:NM_001323579.1_+_peak206560 20:42285456 4.765160
4  20:42274422:42274503:NM_001323580.1_+_peak206560 20:42285456 4.765160
5     20:42274422:42274503:NM_016004.4_+_peak206560 20:42285456 4.765160
6  20:42274422:42274503:NM_001323581.1_+_peak206560 20:42285456 4.580211
7   6:11210211:11210296:NM_001271033.1_-_peak282900  6:11212754 4.568665
8      6:11210211:11210296:NM_006403.3_-_peak282900  6:11212754 4.549482
9  20:42274422:42274503:NM_001303458.2_+_peak206560 20:42285456 4.511515
10  6:11210211:11210296:NM_001142393.1_-_peak282900  6:11212754 4.478218</code></pre>
<p>The top QTLs are really one in multiple genes.</p>
<pre class="bash"><code>#unzip the chrom 20 vcf 
gunzip /project2/gilad/briana/YRI_geno_hg19/chr20.dose.filt.vcf.gz
python filter_geno.py  20 42285456 /project2/gilad/briana/threeprimeseq/data/filtered_geno/chrom20pos42285456.vcf
#rezip bgzip- load three-prime-env</code></pre>
<pre class="r"><code>samples=c(&quot;NA18486&quot;,&quot;NA18505&quot;, &#39;NA18508&#39;,&#39;NA18511&#39;,&#39;NA18519&#39;,&#39;NA18520&#39;,&#39;NA18853&#39;,&#39;NA18858&#39;,&#39;NA18861&#39;,&#39;NA18870&#39;,&#39;NA18909&#39;,&#39;NA18916&#39;,&#39;NA19119&#39;,&#39;NA19128&#39;,&#39;NA19130&#39;,&#39;NA19141&#39;,&#39;NA19160&#39;,&#39;NA19209&#39;,&#39;NA19210&#39;,&#39;NA19223&#39;,&#39;NA19225&#39;,&#39;NA19238&#39;,&#39;NA19239&#39;,&#39;NA19257&#39;)
geno_names=c(&#39;CHROM&#39;, &#39;POS&#39;, &#39;snpID&#39;, &#39;REF&#39;, &#39;ALT&#39;, &#39;QUAL&#39;, &#39;FILTER&#39;, &#39;INFO&#39;, &#39;FORMAT&#39;, &#39;NA18486&#39;, &#39;NA18487&#39;, &#39;NA18488&#39;, &#39;NA18489&#39;, &#39;NA18498&#39;, &#39;NA18499&#39;, &#39;NA18501&#39;, &#39;NA18502&#39;, &#39;NA18504&#39;, &#39;NA18505&#39;, &#39;NA18507&#39;, &#39;NA18508&#39;, &#39;NA18510&#39;, &#39;NA18511&#39;, &#39;NA18516&#39;, &#39;NA18517&#39;, &#39;NA18519&#39;, &#39;NA18520&#39;, &#39;NA18522&#39;, &#39;NA18523&#39;, &#39;NA18852&#39;, &#39;NA18853&#39;, &#39;NA18855&#39;, &#39;NA18856&#39;, &#39;NA18858&#39;, &#39;NA18859&#39;, &#39;NA18861&#39;, &#39;NA18862&#39;, &#39;NA18867&#39;, &#39;NA18868&#39;, &#39;NA18870&#39;, &#39;NA18871&#39;, &#39;NA18873&#39;, &#39;NA18874&#39;, &#39;NA18907&#39;, &#39;NA18909&#39;, &#39;NA18910&#39;, &#39;NA18912&#39;, &#39;NA18913&#39;, &#39;NA18916&#39;, &#39;NA18917&#39;, &#39;NA18923&#39;, &#39;NA18924&#39;, &#39;NA18933&#39;, &#39;NA18934&#39;, &#39;NA19093&#39;, &#39;NA19095&#39;, &#39;NA19096&#39;, &#39;NA19098&#39;, &#39;NA19099&#39;, &#39;NA19101&#39;, &#39;NA19102&#39;, &#39;NA19107&#39;, &#39;NA19108&#39;, &#39;NA19113&#39;, &#39;NA19114&#39;, &#39;NA19116&#39;, &#39;NA19117&#39;, &#39;NA19118&#39;, &#39;NA19119&#39;, &#39;NA19121&#39;, &#39;NA19122&#39;, &#39;NA19127&#39;, &#39;NA19128&#39;, &#39;NA19129&#39;, &#39;NA19130&#39;, &#39;NA19131&#39;, &#39;NA19137&#39;, &#39;NA19138&#39;, &#39;NA19140&#39;, &#39;NA19141&#39;, &#39;NA19143&#39;, &#39;NA19144&#39;, &#39;NA19146&#39;, &#39;NA19147&#39;, &#39;NA19149&#39;, &#39;NA19150&#39;, &#39;NA19152&#39;, &#39;NA19153&#39;, &#39;NA19159&#39;, &#39;NA19160&#39;, &#39;NA19171&#39;, &#39;NA19172&#39;, &#39;NA19175&#39;, &#39;NA19176&#39;, &#39;NA19184&#39;, &#39;NA19185&#39;, &#39;NA19189&#39;, &#39;NA19190&#39;, &#39;NA19197&#39;, &#39;NA19198&#39;, &#39;NA19200&#39;, &#39;NA19201&#39;, &#39;NA19203&#39;, &#39;NA19204&#39;, &#39;NA19206&#39;, &#39;NA19207&#39;, &#39;NA19209&#39;, &#39;NA19210&#39;, &#39;NA19213&#39;, &#39;NA19214&#39;, &#39;NA19222&#39;, &#39;NA19223&#39;, &#39;NA19225&#39;, &#39;NA19226&#39;, &#39;NA19235&#39;, &#39;NA19236&#39;, &#39;NA19238&#39;, &#39;NA19239&#39;, &#39;NA19247&#39;, &#39;NA19248&#39;, &#39;NA19256&#39;, &#39;NA19257&#39;)

chr20.42285456geno=read.table(&quot;../data/perm_QTL_opp/chrom20pos42285456.vcf&quot;, col.names=geno_names, stringsAsFactors = F) %&gt;% select(one_of(samples))

chr20.42285456geno_anno=read.table(&quot;../data/perm_QTL_opp/chrom20pos42285456.vcf&quot;, col.names=geno_names, stringsAsFactors = F) %&gt;% select(CHROM, POS, snpID, REF, ALT, QUAL, FILTER, INFO, FORMAT)

chr20.42285456geno_dose=apply(chr20.42285456geno, 2, function(y)sapply(y, function(x)as.integer(strsplit(x,&quot;:&quot;)[[1]][[2]])))


chr20.42285456geno_dose_full=data.frame(cbind(chr20.42285456geno_anno, chr20.42285456geno_dose))</code></pre>
<p>Grep the pheno type:</p>
<pre class="r"><code># find the phentpye values for 20:42274422:42274503:NM_001303459.2_+_peak206560
#grep -F &quot;20:42274422:42274503:NM_001303459.2_+_peak206560&quot; filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear.pheno_fixed.txt.gz.phen_chr20 &gt; ../qtl_example/nuc_peak206560

pheno206560= read.table(&quot;../data/perm_QTL_opp/nuc_peak206560&quot;, stringsAsFactors = F, col.names = c(&#39;Chr&#39;,  &#39;start&#39;,    &#39;end&#39;,  &#39;ID&#39;,   &#39;NA18486&#39;,  &#39;NA18497&#39;,  &#39;NA18500&#39;,  &#39;NA18505&#39;,&#39;NA18508&#39; ,&#39;NA18511&#39;, &#39;NA18519&#39;,  &#39;NA18520&#39;,  &#39;NA18853&#39;,  &#39;NA18858&#39;,  &#39;NA18861&#39;   ,&#39;NA18870&#39;, &#39;NA18909&#39;,  &#39;NA18916&#39;,  &#39;NA19092&#39;,  &#39;NA19119&#39;,  &#39;NA19128&#39;   ,&#39;NA19130&#39;, &#39;NA19141&#39;   ,&#39;NA19160&#39;, &#39;NA19193&#39;,  &#39;NA19209&#39;   ,&#39;NA19210&#39;, &#39;NA19223&#39;   ,&#39;NA19225&#39;, &#39;NA19238&#39;,  &#39;NA19239&#39;   , &#39;NA19257&#39;))

pheno206560= pheno206560 %&gt;% select(one_of(samples))


geno206560=chr20.42285456geno_dose_full[which(chr20.42285456geno_dose_full$POS==42285456),10:33]


for_plot206560=data.frame(bind_rows(geno206560,pheno206560) %&gt;% t)
colnames(for_plot206560)=c(&quot;Genotype&quot;, &quot;PAS&quot;)
for_plot206560$Genotype=as.factor(for_plot206560$Genotype)


ggplot(for_plot206560, aes(x=Genotype, y=PAS, fill=Genotype, group=Genotype)) + geom_boxplot() + labs(x=&quot;Genotype&quot;, title=&quot;20:42274422:42274503:NM_001303459.2_+_peak206560 QTL&quot;) + geom_jitter( aes(x=Genotype, y=PAS))</code></pre>
<p><img src="figure/peakOverlap_oppstrand.Rmd/unnamed-chunk-36-1.png" width="672" style="display: block; margin: auto;" /></p>
<p>6:11210211:11210296:NM_001271033.1_-_peak282900 6:11212754</p>
<pre class="bash"><code>#unzip the chrom 6 vcf 
gunzip /project2/gilad/briana/YRI_geno_hg19/chr6.dose.filt.vcf.gz
python filter_geno.py  6 11212754 /project2/gilad/briana/threeprimeseq/data/filtered_geno/chrom6pos11212754.vcf
#rezip bgzip- load three-prime-env</code></pre>
<p>Prepare genotypes</p>
<pre class="r"><code>chr6.11212754geno=read.table(&quot;../data/perm_QTL_opp/chrom6pos11212754.vcf&quot;, col.names=geno_names, stringsAsFactors = F) %&gt;% select(one_of(samples))

chr6.11212754geno_anno=read.table(&quot;../data/perm_QTL_opp/chrom6pos11212754.vcf&quot;, col.names=geno_names, stringsAsFactors = F) %&gt;% select(CHROM, POS, snpID, REF, ALT, QUAL, FILTER, INFO, FORMAT)

chr6.11212754geno_dose=apply(chr6.11212754geno, 2, function(y)sapply(y, function(x)as.integer(strsplit(x,&quot;:&quot;)[[1]][[2]])))


chr6.11212754geno_dose_full=data.frame(cbind(chr6.11212754geno_anno, chr6.11212754geno_dose))

geno282900=chr6.11212754geno_dose_full[which(chr6.11212754geno_dose_full$POS==11212754),10:33]</code></pre>
<p>Prepare Phenotypes</p>
<pre class="r"><code># find the phentpye values for 6:11210211:11210296:NM_001271033.1_-_peak282900
#grep -F &quot;6:11210211:11210296:NM_001271033.1_-_peak282900&quot; filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Nuclear.pheno_fixed.txt.gz.phen_chr6 &gt; ../qtl_example/nuc_peak282900

phen_names= c(&#39;Chr&#39;,  &#39;start&#39;,    &#39;end&#39;,  &#39;ID&#39;,   &#39;NA18486&#39;,  &#39;NA18497&#39;,  &#39;NA18500&#39;,  &#39;NA18505&#39;,&#39;NA18508&#39; ,&#39;NA18511&#39;, &#39;NA18519&#39;,  &#39;NA18520&#39;,  &#39;NA18853&#39;,  &#39;NA18858&#39;,  &#39;NA18861&#39;   ,&#39;NA18870&#39;, &#39;NA18909&#39;,  &#39;NA18916&#39;,  &#39;NA19092&#39;,  &#39;NA19119&#39;,  &#39;NA19128&#39;   ,&#39;NA19130&#39;, &#39;NA19141&#39;   ,&#39;NA19160&#39;, &#39;NA19193&#39;,  &#39;NA19209&#39;   ,&#39;NA19210&#39;, &#39;NA19223&#39;   ,&#39;NA19225&#39;, &#39;NA19238&#39;,  &#39;NA19239&#39;   , &#39;NA19257&#39;)

pheno282900= read.table(&quot;../data/perm_QTL_opp/nuc_peak282900&quot;, stringsAsFactors = F, col.names = phen_names)

pheno282900= pheno282900 %&gt;% select(one_of(samples))



for_plot282900=data.frame(bind_rows(geno282900,pheno282900) %&gt;% t)
colnames(for_plot282900)=c(&quot;Genotype&quot;, &quot;PAS&quot;)
for_plot282900$Genotype=as.factor(for_plot282900$Genotype)


ggplot(for_plot282900, aes(x=Genotype, y=PAS, fill=Genotype, group=Genotype)) + geom_boxplot() + labs(x=&quot;Genotype&quot;, title=&quot;6:11210211:11210296:NM_001271033.1_-_peak282900 QTL&quot;) + geom_jitter( aes(x=Genotype, y=PAS))</code></pre>
<p><img src="figure/peakOverlap_oppstrand.Rmd/unnamed-chunk-39-1.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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] bindrcpp_0.2.2  reshape2_1.4.3  cowplot_0.9.3   workflowr_1.1.1
 [5] forcats_0.3.0   stringr_1.3.1   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  
 [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      bindr_0.1.1      
[16] plyr_1.8.4        munsell_0.5.0     gtable_0.2.0     
[19] cellranger_1.1.0  rvest_0.3.2       R.methodsS3_1.7.1
[22] evaluate_0.11     labeling_0.3      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   </code></pre>
</div>

<hr>
<p>
    
</p>
<hr>

<!-- To enable disqus, uncomment the section below and provide your disqus_shortname -->

<!-- disqus
  <div id="disqus_thread"></div>
    <script type="text/javascript">
        /* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */
        var disqus_shortname = 'rmarkdown'; // required: replace example with your forum shortname

        /* * * DON'T EDIT BELOW THIS LINE * * */
        (function() {
            var dsq = document.createElement('script'); dsq.type = 'text/javascript'; dsq.async = true;
            dsq.src = '//' + disqus_shortname + '.disqus.com/embed.js';
            (document.getElementsByTagName('head')[0] || document.getElementsByTagName('body')[0]).appendChild(dsq);
        })();
    </script>
    <noscript>Please enable JavaScript to view the <a href="http://disqus.com/?ref_noscript">comments powered by Disqus.</a></noscript>
    <a href="http://disqus.com" class="dsq-brlink">comments powered by <span class="logo-disqus">Disqus</span></a>
-->
<!-- Adjust MathJax settings so that all math formulae are shown using
TeX fonts only; see
http://docs.mathjax.org/en/latest/configuration.html.  This will make
the presentation more consistent at the cost of the webpage sometimes
taking slightly longer to load. Note that this only works because the
footer is added to webpages before the MathJax javascript. -->
<script type="text/x-mathjax-config">
  MathJax.Hub.Config({
    "HTML-CSS": { availableFonts: ["TeX"] }
  });
</script>

<hr>
<p>
  This reproducible <a href="http://rmarkdown.rstudio.com">R Markdown</a>
  analysis was created with
  <a href="https://github.com/jdblischak/workflowr">workflowr</a> 1.1.1
</p>
<hr>


</div>
</div>

</div>

<script>

// add bootstrap table styles to pandoc tables
function bootstrapStylePandocTables() {
  $('tr.header').parent('thead').parent('table').addClass('table table-condensed');
}
$(document).ready(function () {
  bootstrapStylePandocTables();
});


</script>

<!-- dynamically load mathjax for compatibility with self-contained -->
<script>
  (function () {
    var script = document.createElement("script");
    script.type = "text/javascript";
    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
    document.getElementsByTagName("head")[0].appendChild(script);
  })();
</script>

</body>
</html>