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<h1 class="title toc-ignore">Peak coverage along Gene examples</h1>
<h4 class="author"><em>Briana Mittleman</em></h4>
<h4 class="date"><em>11/12/2018</em></h4>
</div>
<p><strong>Last updated:</strong> 2018-11-14</p>
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Unstaged changes:
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</code></pre>
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<details> <summary> <small><strong>Expand here to see past versions:</strong></small> </summary>
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Rmd
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<a href="https://github.com/brimittleman/threeprimeseq/blob/f3efe0fc9d70bf7f3fae12b9c17fdbeaf13f132b/analysis/peakGeneCovEx.Rmd" target="_blank">f3efe0f</a>
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<td style="text-align:left;">
Briana Mittleman
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<td style="text-align:left;">
2018-11-14
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add prop cov plots
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<a href="https://cdn.rawgit.com/brimittleman/threeprimeseq/d4ff15bb6d39d47797377a541e5d96e49afaa4be/docs/peakGeneCovEx.html" target="_blank">d4ff15b</a>
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Briana Mittleman
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2018-11-14
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Build site.
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<a href="https://github.com/brimittleman/threeprimeseq/blob/acc74e7fc3a97b1f103febe1503c47a4a7d65402/analysis/peakGeneCovEx.Rmd" target="_blank">acc74e7</a>
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<td style="text-align:left;">
Briana Mittleman
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2018-11-14
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add example by genotype plots
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html
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<a href="https://cdn.rawgit.com/brimittleman/threeprimeseq/d254fd2658506a41fe9d2afe08d945f4d0e3fab8/docs/peakGeneCovEx.html" target="_blank">d254fd2</a>
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<td style="text-align:left;">
Briana Mittleman
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<td style="text-align:left;">
2018-11-13
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Build site.
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<a href="https://github.com/brimittleman/threeprimeseq/blob/42991264aeb6f5c4095a40de261e9911fcf125a0/analysis/peakGeneCovEx.Rmd" target="_blank">4299126</a>
</td>
<td style="text-align:left;">
Briana Mittleman
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<td style="text-align:left;">
2018-11-13
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<td style="text-align:left;">
investigate which peak is sig
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html
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<a href="https://cdn.rawgit.com/brimittleman/threeprimeseq/e2da5c45d30dcb3df83e5b4abcf50bd0e8bdc675/docs/peakGeneCovEx.html" target="_blank">e2da5c4</a>
</td>
<td style="text-align:left;">
Briana Mittleman
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<td style="text-align:left;">
2018-11-13
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Build site.
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<a href="https://github.com/brimittleman/threeprimeseq/blob/f8d8c94dfe19af73199a5127e413af9e28859250/analysis/peakGeneCovEx.Rmd" target="_blank">f8d8c94</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-13
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<td style="text-align:left;">
add plots for peak coverage
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html
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<a href="https://cdn.rawgit.com/brimittleman/threeprimeseq/24821f2e800b26456114ff1400996f16ebbaff3d/docs/peakGeneCovEx.html" target="_blank">24821f2</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-12
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Build site.
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Rmd
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<a href="https://github.com/brimittleman/threeprimeseq/blob/7d1bd9ac68750e5346974f06ef39a47987716f90/analysis/peakGeneCovEx.Rmd" target="_blank">7d1bd9a</a>
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<td style="text-align:left;">
Briana Mittleman
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2018-11-12
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add code for looking at sig gene peaks
</td>
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</ul>
<p></details></p>
<hr />
<p>The quantified peak files are:</p>
<ul>
<li>/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc</li>
<li>/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc</li>
</ul>
<p>I want to grep specific genes and look at the read distribution for peaks along a gene. In these files the peakIDs stil have the peak locations. Before I ran the QTL analysis I changed the final coverage (ex /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.gz) to have the TSS as the ID.</p>
<p>Librarys</p>
<pre class="r"><code>library(workflowr)</code></pre>
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✔ readr 1.1.1 ✔ forcats 0.3.0</code></pre>
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ggsave</code></pre>
<pre class="r"><code>nuc_names=c('Geneid', 'Chr', 'Start', 'End', 'Strand', 'Length', 'NA18486' ,'NA18497', 'NA18500' ,'NA18505', 'NA18508' ,'NA18511', 'NA18519', 'NA18520', 'NA18853','NA18858', 'NA18861', 'NA18870' ,'NA18909' ,'NA18912' ,'NA18916', 'NA19092' ,'NA19093', 'NA19119', 'NA19128' ,'NA19130', 'NA19131' ,'NA19137', 'NA19140', 'NA19141' ,'NA19144', 'NA19152' ,'NA19153', 'NA19160' ,'NA19171', 'NA19193' ,'NA19200', 'NA19207', 'NA19209', 'NA19210', 'NA19223' ,'NA19225', 'NA19238' ,'NA19239', 'NA19257')
tot_names=c('Geneid', 'Chr', 'Start', 'End', 'Strand', 'Length', 'NA18486' ,'NA18497', 'NA18500' ,'NA18505', 'NA18508' ,'NA18511', 'NA18519', 'NA18520', 'NA18853','NA18858', 'NA18861', 'NA18870' ,'NA18909' ,'NA18912' ,'NA18916', 'NA19092' ,'NA19093', 'NA19119', 'NA19128' ,'NA19130', 'NA19131' ,'NA19137', 'NA19140', 'NA19141' ,'NA19144', 'NA19152' ,'NA19153', 'NA19160' ,'NA19171', 'NA19193' ,'NA19200', 'NA19207', 'NA19209', 'NA19210', 'NA19223' ,'NA19225', 'NA19238' ,'NA19239', 'NA19257')</code></pre>
<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) %>% mutate(sig=ifelse(-log10(bh)>=1, 1,0 )) %>% separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% filter(sig==1)
totalAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_transcript_permResBH.txt", stringsAsFactors = F, header=T) %>% mutate(sig=ifelse(-log10(bh)>=1, 1,0 )) %>% separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% filter(sig==1)</code></pre>
<p>examples to look at Nuclear: IRF5, HSF1, NOL9,DCAF16,</p>
<p>Total: NBEAL2, SACM1L, COX7A2L</p>
<pre class="bash"><code>
#nuclear
grep IRF5 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/IRF5_NuclearCov_peaks.txt
grep HSF1 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/HSF1_NuclearCov_peaks.txt
grep NOL9 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/NOL9_NuclearCov_peaks.txt
grep DCAF16 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/DCAF16_NuclearCov_peaks.txt
grep PPP4C /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/PPP4C_NuclearCov_peaks.txt
#total
grep NBEAL2 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/NBEAL2_TotalCov_peaks.txt
grep SACM1L /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/SACM1L_TotalCov_peaks.txt
grep TESK1 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/TESK1_TotalCov_peaks.txt
grep DGCR14 /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/DGCR14_TotalCov_peaks.txt
</code></pre>
<p>Copy these to my computer so I can work with them here. I am going to want to make a function that makes the histogram reproducibly for anyfile. I will need to know how many bins to include in the histogram. First I will make the graph for one example then I will make it more general.</p>
<p>Files are in /Users/bmittleman1/Documents/Gilad_lab/threeprimeseq/data/example_gene_peakQuant</p>
<p>Start wit a small file.</p>
<pre class="r"><code>pos=c(3,4,7:39)
PPP4c=read.table("../data/example_gene_peakQuant/PPP4C_NuclearCov_peaks.txt", stringsAsFactors = F, col.names = nuc_names) %>% select(pos)
PPP4c$peaks=seq(0, (nrow(PPP4c)-1))
PPP4c_melt=melt(PPP4c, id.vars=c('peaks','Start','End'))</code></pre>
<p>Plot:</p>
<pre class="r"><code>ggplot(PPP4c_melt, aes(x=peaks, y=value, by=variable, fill=variable)) + geom_histogram(stat="identity")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<p><img src="figure/peakGeneCovEx.Rmd/unnamed-chunk-6-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-6-1.png:</em></summary>
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<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/d4ff15bb6d39d47797377a541e5d96e49afaa4be/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-6-1.png" target="_blank">d4ff15b</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-14
</td>
</tr>
<tr>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/e2da5c45d30dcb3df83e5b4abcf50bd0e8bdc675/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-6-1.png" target="_blank">e2da5c4</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-13
</td>
</tr>
</tbody>
</table>
<p></details></p>
<p>Try with actual location as the center of the peak.</p>
<pre class="r"><code>pos=c(3,4,7:39)
PPP4c_2=read.table("../data/example_gene_peakQuant/PPP4C_NuclearCov_peaks.txt", stringsAsFactors = F, col.names = tot_names) %>% select(pos)
PPP4c_2$peaks=seq(0, (nrow(PPP4c_2)-1))
PPP4c_2= PPP4c_2 %>% mutate(PeakCenter=(Start+ (End-Start)/2))
PPP4c2_melt=melt(PPP4c_2, id.vars=c('peaks','PeakCenter', "Start", "End"))
colnames(PPP4c2_melt)= c('peaks','PeakCenter', "Start", "End", "Individual", "ReadCount")</code></pre>
<p>Plot:</p>
<pre class="r"><code>ggplot(PPP4c2_melt, aes(x=PeakCenter, y=ReadCount, by=Individual, fill=Individual)) + geom_histogram(stat="identity") + labs(title="Peak Coverage and Location PP4c")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<p><img src="figure/peakGeneCovEx.Rmd/unnamed-chunk-8-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-8-1.png:</em></summary>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/d4ff15bb6d39d47797377a541e5d96e49afaa4be/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-8-1.png" target="_blank">d4ff15b</a>
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<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-14
</td>
</tr>
<tr>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/e2da5c45d30dcb3df83e5b4abcf50bd0e8bdc675/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-8-1.png" target="_blank">e2da5c4</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-13
</td>
</tr>
</tbody>
</table>
<p></details></p>
<p>Generalize this for more genes:</p>
<pre class="r"><code>makePeakLocplot=function(file, geneName,fraction){
pos=c(3,4,7:39)
if (fraction=="Total"){
gene=read.table(file, stringsAsFactors = F, col.names = tot_names) %>% select(pos)
}
else{
gene=read.table(file, stringsAsFactors = F, col.names = nuc_names) %>% select(pos)
}
gene$peaks=seq(0, (nrow(gene)-1))
gene= gene %>% mutate(PeakCenter=(Start+ (End-Start)/2))
gene_melt=melt(gene, id.vars=c('peaks','PeakCenter', "Start", "End"))
colnames(gene_melt)= c('peaks','PeakCenter', "Start", "End", "Individual", "ReadCount")
finalplot=ggplot(gene_melt, aes(x=PeakCenter, y=ReadCount, by=Individual, fill=Individual)) + geom_histogram(stat="identity", show.legend = FALSE) + labs(title=paste("Peak Coverage and Location", geneName, sep = " "))
return(finalplot)
}</code></pre>
<p>Try for another gene:</p>
<pre class="r"><code>makePeakLocplot("../data/example_gene_peakQuant/PPP4C_NuclearCov_peaks.txt",'PPP4c',"Nuclear")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<p><img src="figure/peakGeneCovEx.Rmd/unnamed-chunk-10-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-10-1.png:</em></summary>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/e2da5c45d30dcb3df83e5b4abcf50bd0e8bdc675/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-10-1.png" target="_blank">e2da5c4</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-13
</td>
</tr>
</tbody>
</table>
<p></details></p>
<pre class="r"><code>makePeakLocplot("../data/example_gene_peakQuant/DCAF16_NuclearCov_peaks.txt",'DCAF16',"Nuclear")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<p><img src="figure/peakGeneCovEx.Rmd/unnamed-chunk-11-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-11-1.png:</em></summary>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/e2da5c45d30dcb3df83e5b4abcf50bd0e8bdc675/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-11-1.png" target="_blank">e2da5c4</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-13
</td>
</tr>
</tbody>
</table>
<p></details></p>
<pre class="r"><code>makePeakLocplot("../data/example_gene_peakQuant/DGCR14_TotalCov_peaks.txt",'DGCR14',"Total")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<p><img src="figure/peakGeneCovEx.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>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/e2da5c45d30dcb3df83e5b4abcf50bd0e8bdc675/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-12-1.png" target="_blank">e2da5c4</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-13
</td>
</tr>
</tbody>
</table>
<p></details></p>
<pre class="r"><code>makePeakLocplot("../data/example_gene_peakQuant/IRF5_NuclearCov_peaks.txt",'IRF5',"Nuclear")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<p><img src="figure/peakGeneCovEx.Rmd/unnamed-chunk-13-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-13-1.png:</em></summary>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/e2da5c45d30dcb3df83e5b4abcf50bd0e8bdc675/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-13-1.png" target="_blank">e2da5c4</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-13
</td>
</tr>
</tbody>
</table>
<p></details></p>
<pre class="r"><code>makePeakLocplot("../data/example_gene_peakQuant/HSF1_NuclearCov_peaks.txt",'HSF1',"Nuclear")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<p><img src="figure/peakGeneCovEx.Rmd/unnamed-chunk-14-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-14-1.png:</em></summary>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/e2da5c45d30dcb3df83e5b4abcf50bd0e8bdc675/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-14-1.png" target="_blank">e2da5c4</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-13
</td>
</tr>
</tbody>
</table>
<p></details></p>
<pre class="r"><code>makePeakLocplot("../data/example_gene_peakQuant/NOL9_NuclearCov_peaks.txt",'NOL9',"Nuclear")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<p><img src="figure/peakGeneCovEx.Rmd/unnamed-chunk-15-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-15-1.png:</em></summary>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/e2da5c45d30dcb3df83e5b4abcf50bd0e8bdc675/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-15-1.png" target="_blank">e2da5c4</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-13
</td>
</tr>
</tbody>
</table>
<p></details></p>
<pre class="r"><code>makePeakLocplot("../data/example_gene_peakQuant/SACM1L_TotalCov_peaks.txt",'SACM1L',"Total")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<p><img src="figure/peakGeneCovEx.Rmd/unnamed-chunk-16-1.png" width="672" style="display: block; margin: auto;" /></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/e2da5c45d30dcb3df83e5b4abcf50bd0e8bdc675/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-16-1.png" target="_blank">e2da5c4</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-13
</td>
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</tbody>
</table>
<p></details></p>
<p>Make a function to do this by peak number (ignoring direction)</p>
<pre class="r"><code>makePeakNumplot=function(file, geneName,fraction){
pos=c(7:39)
if (fraction=="Total"){
gene=read.table(file, stringsAsFactors = F, col.names = tot_names) %>% select(pos)
}
else{
gene=read.table(file, stringsAsFactors = F, col.names = nuc_names) %>% select(pos)
}
gene$peaks=seq(0, (nrow(gene)-1))
gene_melt=melt(gene, id.vars=c('peaks'))
colnames(gene_melt)= c('peaks',"Individual", "ReadCount")
finalplot=ggplot(gene_melt, aes(x=peaks, y=ReadCount, by=Individual, fill=Individual)) + geom_histogram(stat="identity", show.legend = FALSE) + labs(title=paste("Peak Coverage", geneName, sep = " "))
return(finalplot)
}</code></pre>
<p>I can plot them next to eachother using cowplot</p>
<pre class="r"><code>ppp4c_loc=makePeakLocplot("../data/example_gene_peakQuant/PPP4C_NuclearCov_peaks.txt",'PPP4c',"Nuclear")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<pre class="r"><code>ppp4c_num=makePeakNumplot("../data/example_gene_peakQuant/PPP4C_NuclearCov_peaks.txt",'PPP4c',"Nuclear")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<pre class="r"><code>plot_grid(ppp4c_loc,ppp4c_num)</code></pre>
<p><img src="figure/peakGeneCovEx.Rmd/unnamed-chunk-18-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-18-1.png:</em></summary>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/e2da5c45d30dcb3df83e5b4abcf50bd0e8bdc675/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-18-1.png" target="_blank">e2da5c4</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-13
</td>
</tr>
</tbody>
</table>
<p></details></p>
<pre class="r"><code>dcaf16_loc=makePeakLocplot("../data/example_gene_peakQuant/DCAF16_NuclearCov_peaks.txt",'DCAF16',"Nuclear")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<pre class="r"><code>dcaf16_num=makePeakNumplot("../data/example_gene_peakQuant/DCAF16_NuclearCov_peaks.txt",'DCAF16',"Nuclear")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<pre class="r"><code>plot_grid(dcaf16_loc,dcaf16_num)</code></pre>
<p><img src="figure/peakGeneCovEx.Rmd/unnamed-chunk-19-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-19-1.png:</em></summary>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/e2da5c45d30dcb3df83e5b4abcf50bd0e8bdc675/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-19-1.png" target="_blank">e2da5c4</a>
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<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-13
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<p></details></p>
<pre class="r"><code>dgcr14_loc=makePeakLocplot("../data/example_gene_peakQuant/DGCR14_TotalCov_peaks.txt",'DGCR14',"Total")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<pre class="r"><code>dgcr14_num=makePeakNumplot("../data/example_gene_peakQuant/DGCR14_TotalCov_peaks.txt",'DGCR14',"Total")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<pre class="r"><code>plot_grid(dgcr14_loc,dgcr14_num)</code></pre>
<p><img src="figure/peakGeneCovEx.Rmd/unnamed-chunk-20-1.png" width="672" style="display: block; margin: auto;" /></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/e2da5c45d30dcb3df83e5b4abcf50bd0e8bdc675/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-20-1.png" target="_blank">e2da5c4</a>
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<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-13
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<p></details></p>
<pre class="r"><code>irf5_loc=makePeakLocplot("../data/example_gene_peakQuant/IRF5_NuclearCov_peaks.txt",'IRF5',"Nuclear")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<pre class="r"><code>irf5_num=makePeakNumplot("../data/example_gene_peakQuant/IRF5_NuclearCov_peaks.txt",'IRF5',"Nuclear")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<pre class="r"><code>plot_grid(irf5_loc,irf5_num)</code></pre>
<p><img src="figure/peakGeneCovEx.Rmd/unnamed-chunk-21-1.png" width="672" style="display: block; margin: auto;" /></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/e2da5c45d30dcb3df83e5b4abcf50bd0e8bdc675/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-21-1.png" target="_blank">e2da5c4</a>
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<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-13
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<p></details></p>
<pre class="r"><code>HSF1_loc=makePeakLocplot("../data/example_gene_peakQuant/HSF1_NuclearCov_peaks.txt",'HSF1',"Nuclear")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<pre class="r"><code>HSF1_num=makePeakNumplot("../data/example_gene_peakQuant/HSF1_NuclearCov_peaks.txt",'HSF1',"Nuclear")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<pre class="r"><code>plot_grid(HSF1_loc,HSF1_num)</code></pre>
<p><img src="figure/peakGeneCovEx.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>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/e2da5c45d30dcb3df83e5b4abcf50bd0e8bdc675/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-22-1.png" target="_blank">e2da5c4</a>
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<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-13
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<p></details></p>
<pre class="r"><code>NOL9_loc=makePeakLocplot("../data/example_gene_peakQuant/NOL9_NuclearCov_peaks.txt",'NOL9',"Nuclear")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<pre class="r"><code>NOL9_num=makePeakNumplot("../data/example_gene_peakQuant/NOL9_NuclearCov_peaks.txt",'NOL9',"Nuclear")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<pre class="r"><code>plot_grid(NOL9_loc,NOL9_num)</code></pre>
<p><img src="figure/peakGeneCovEx.Rmd/unnamed-chunk-23-1.png" width="672" style="display: block; margin: auto;" /></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/e2da5c45d30dcb3df83e5b4abcf50bd0e8bdc675/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-23-1.png" target="_blank">e2da5c4</a>
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<td style="text-align:left;">
Briana Mittleman
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<td style="text-align:left;">
2018-11-13
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<p></details></p>
<pre class="r"><code>SACM1L_loc=makePeakLocplot("../data/example_gene_peakQuant/SACM1L_TotalCov_peaks.txt",'SACM1L',"Total")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<pre class="r"><code>SACM1L_num=makePeakNumplot("../data/example_gene_peakQuant/SACM1L_TotalCov_peaks.txt",'SACM1L',"Total")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<pre class="r"><code>plot_grid(SACM1L_loc,SACM1L_num)</code></pre>
<p><img src="figure/peakGeneCovEx.Rmd/unnamed-chunk-24-1.png" width="672" style="display: block; margin: auto;" /></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/e2da5c45d30dcb3df83e5b4abcf50bd0e8bdc675/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-24-1.png" target="_blank">e2da5c4</a>
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<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-11-13
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<p></details></p>
<pre class="r"><code>NBEAL2_loc=makePeakLocplot("../data/example_gene_peakQuant/NBEAL2_TotalCov_peaks.txt",'NBEAL2',"Total")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<pre class="r"><code>NBEAL2_num=makePeakNumplot("../data/example_gene_peakQuant/NBEAL2_TotalCov_peaks.txt",'NBEAL2',"Total")</code></pre>
<pre><code>Warning: Ignoring unknown parameters: binwidth, bins, pad</code></pre>
<pre class="r"><code>plot_grid(NBEAL2_loc,NBEAL2_num)</code></pre>
<p><img src="figure/peakGeneCovEx.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>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/d254fd2658506a41fe9d2afe08d945f4d0e3fab8/docs/figure/peakGeneCovEx.Rmd/unnamed-chunk-25-1.png" target="_blank">d254fd2</a>
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<td style="text-align:left;">
Briana Mittleman
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2018-11-13
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<p></details></p>
<div id="which-peak-is-sig" class="section level2">
<h2>Which Peak is Sig</h2>
<p>It would be interesting to know which peak in these gene plots is associated with the QTL.</p>
<p>Nuclear: * IRF5 : peak305794-7:128635754, peak305795,128681297, peak305798-7:128661132</p>
<pre class="r"><code>IRF5_all=read.table("../data/example_gene_peakQuant/IRF5_NuclearCov_peaks.txt", col.names = nuc_names)</code></pre>
<p>peak305794-peak 4 peak305795-peak 5 peak305798-peak 6</p>
<ul>
<li>HSF1: peak323832- 8:145516593</li>
</ul>
<pre class="r"><code>HSF1_all=read.table("../data/example_gene_peakQuant/HSF1_NuclearCov_peaks.txt", col.names = nuc_names)</code></pre>
<p>The QTL is the first peak. (peak 0)</p>
<ul>
<li>NOL9: peak702- 1:6604621</li>
</ul>
<pre class="r"><code>NOL9_all=read.table("../data/example_gene_peakQuant/NOL9_NuclearCov_peaks.txt", col.names = nuc_names)</code></pre>
<p>QTL is peak 7 in the graph</p>
<ul>
<li>DCAF16: peak236311- 4:17797455</li>
</ul>
<pre class="r"><code>DCAF16_all=read.table("../data/example_gene_peakQuant/DCAF16_NuclearCov_peaks.txt", col.names = nuc_names)</code></pre>
<p>QTL is peak 3 in graph</p>
<ul>
<li>PPP4C: peak122195-16:30482494</li>
</ul>
<pre class="r"><code>pprc_all=read.table("../data/example_gene_peakQuant/PPP4C_NuclearCov_peaks.txt", col.names = nuc_names)</code></pre>
<p>The QTL peak is the lower expressed peak (peak1 in graph)</p>
<p>Total: * NBEAL2: peak216374- 3:47080127</p>
<pre class="r"><code>NBEAL2_all=read.table("../data/example_gene_peakQuant/NBEAL2_TotalCov_peaks.txt", col.names = tot_names)</code></pre>
<p>peak 15 in graph</p>
<ul>
<li>SACM1L: peak216084-3:45780980, peak216086-3:45780980, peak216087-3:45790569</li>
</ul>
<pre class="r"><code>SACM1L_all=read.table("../data/example_gene_peakQuant/SACM1L_TotalCov_peaks.txt", col.names = tot_names)</code></pre>
<p>peak216084-12</p>
<p>peak216086 - 14 (major peak)</p>
<p>peak216087 -15</p>
<ul>
<li>DGCR14: peak204736-22:18647341</li>
</ul>
<pre class="r"><code>DGCR14_all=read.table("../data/example_gene_peakQuant/DGCR14_TotalCov_peaks.txt", col.names = tot_names)</code></pre>
<p>peak204736- peak 7</p>
<p>This has shown me that most of the QTL peaks are not the major/most used peak. This leads me to beleive I would get different QTLs if I made one metric per gene because I may ont be able to capture these effects.</p>
</div>
<div id="seperate-by-genotype" class="section level2">
<h2>Seperate by genotype</h2>
<p>It would be good to look at these seperated by genotype.</p>
<ul>
<li>IRF5 : peak305794-7:128635754, peak305795,7:128681297, peak305798-7:128661132</li>
</ul>
<pre class="r"><code>geno_names=c('CHROM', 'POS', 'snpID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', 'NA18486', 'NA18487', 'NA18488', 'NA18489', 'NA18498', 'NA18499', 'NA18501', 'NA18502', 'NA18504', 'NA18505', 'NA18507', 'NA18508', 'NA18510', 'NA18511', 'NA18516', 'NA18517', 'NA18519', 'NA18520', 'NA18522', 'NA18523', 'NA18852', 'NA18853', 'NA18855', 'NA18856', 'NA18858', 'NA18859', 'NA18861', 'NA18862', 'NA18867', 'NA18868', 'NA18870', 'NA18871', 'NA18873', 'NA18874', 'NA18907', 'NA18909', 'NA18910', 'NA18912', 'NA18913', 'NA18916', 'NA18917', 'NA18923', 'NA18924', 'NA18933', 'NA18934', 'NA19093', 'NA19095', 'NA19096', 'NA19098', 'NA19099', 'NA19101', 'NA19102', 'NA19107', 'NA19108', 'NA19113', 'NA19114', 'NA19116', 'NA19117', 'NA19118', 'NA19119', 'NA19121', 'NA19122', 'NA19127', 'NA19128', 'NA19129', 'NA19130', 'NA19131', 'NA19137', 'NA19138', 'NA19140', 'NA19141', 'NA19143', 'NA19144', 'NA19146', 'NA19147', 'NA19149', 'NA19150', 'NA19152', 'NA19153', 'NA19159', 'NA19160', 'NA19171', 'NA19172', 'NA19175', 'NA19176', 'NA19184', 'NA19185', 'NA19189', 'NA19190', 'NA19197', 'NA19198', 'NA19200', 'NA19201', 'NA19203', 'NA19204', 'NA19206', 'NA19207', 'NA19209', 'NA19210', 'NA19213', 'NA19214', 'NA19222', 'NA19223', 'NA19225', 'NA19226', 'NA19235', 'NA19236', 'NA19238', 'NA19239', 'NA19247', 'NA19248', 'NA19256', 'NA19257')
#the samples I ran the QTLs for
samples=c('NA18486','NA18505','NA18508','NA18511','NA18519','NA18520','NA18853','NA18858','NA18861','NA18870','NA18909','NA18912','NA18916','NA19093','NA19119','NA19128','NA19130','NA19131','NA19137','NA19140','NA19141','NA19144','NA19152','NA19153','NA19160','NA19171','NA19200','NA19207','NA19209','NA19210','NA19223','NA19225','NA19238','NA19239','NA19257')</code></pre>
<pre class="bash"><code>#grep the genotpe file results to /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/
grep 7:128635754 chr7.dose.filt.vcf > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/Genotypes_7:128635754.txt
grep 7:128681297 chr7.dose.filt.vcf > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/Genotypes_7:128681297.txt
grep 7:128661132 chr7.dose.filt.vcf > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/Genotypes_7:128661132.txt</code></pre>
<p>Transfer to computer:</p>
<p>Make a function to take a file and format it the way I can use it.</p>
<pre class="r"><code>#this gives me 35x1 data frame with the genotpes for each ind at this snp.
prepare_genotpes=function(file, genName=geno_names, samp=samples){
geno=read.table(file, col.names=genName, stringsAsFactors = F) %>% select(one_of(samp))
geno_dose=apply(geno, 2, function(y)sapply(y, function(x)as.integer(strsplit(x,":")[[1]][[2]])))
geno_dose=as.data.frame(geno_dose) %>% rownames_to_column(var="individual")
return(geno_dose)
}
chr7_128681297= prepare_genotpes("../data/example_gene_peakQuant/Genotypes_7:128681297.txt")</code></pre>
<p>I want a dataframe that has individual, genotype, then all of the peaks. I also need to remove individuals not in that genotype file.</p>
<pre class="r"><code>IRF5_pheno=IRF5_all%>% select(one_of(samples))
row.names(IRF5_pheno)=paste("IRF5_peak", seq(1,nrow(IRF5_all)),sep="_")
IRF5_pheno= IRF5_pheno %>% t
IRF5_pheno= as.data.frame(IRF5_pheno) %>% rownames_to_column(var="individual")
IRF5_pheno_geno=IRF5_pheno %>% inner_join(chr7_128681297, by="individual")
IRF5_pheno_geno_melt= melt(IRF5_pheno_geno, id.vars=c("geno_dose", "individual")) %>% group_by(variable,geno_dose) %>% summarise(mean=mean(value),sd=sd(value))
IRF5_pheno_geno_melt$geno_dose=as.factor(IRF5_pheno_geno_melt$geno_dose)</code></pre>
<pre class="r"><code>irf5_readplot=ggplot(IRF5_pheno_geno_melt,aes(x=variable, y=mean, by=geno_dose, fill=geno_dose)) + geom_bar(stat="identity") +theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(y="Mean read count", x="Peak", title="IRF5 peaks by chr7:128681297 genotype") + annotate("pointrange", x = 6, y = 750, ymin = 750, ymax = 750,
colour = "black", size = 1.5)</code></pre>
<p>Try this with a different gene.</p>
<pre class="bash"><code>#grep the genotpe file results to /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/
grep 16:30482494 chr16.dose.filt.vcf > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/Genotypes_16:30482494.txt
</code></pre>
<pre class="r"><code>chr16_30482494= prepare_genotpes("../data/example_gene_peakQuant/Genotypes_16:30482494.txt")
pprc_pheno=pprc_all%>% select(one_of(samples))
row.names(pprc_pheno)=paste("PPRC_peak", seq(1,nrow(pprc_all)),sep="_")
pprc_pheno= pprc_pheno %>% t
pprc_pheno= as.data.frame(pprc_pheno) %>% rownames_to_column(var="individual")
pprc_pheno_geno=pprc_pheno %>% inner_join(chr16_30482494, by="individual")
pprc_pheno_geno_melt= melt(pprc_pheno_geno, id.vars=c("geno_dose", "individual")) %>% group_by(variable,geno_dose) %>% summarise(mean=mean(value),sd=sd(value))
pprc_pheno_geno_melt$geno_dose=as.factor(pprc_pheno_geno_melt$geno_dose)</code></pre>
<p>Plot</p>
<pre class="r"><code>pprc_countplot=ggplot(pprc_pheno_geno_melt,aes(x=variable, y=mean, by=geno_dose, fill=geno_dose)) + geom_bar(stat="identity") +theme(axis.text.x = element_text(angle = 90, hjust = 1))+ labs(y="Mean read count", x="Peak", title="PPRC peaks by 16:30482494 genotype") + annotate("pointrange", x = 2, y = 20, ymin = 20, ymax = 20,colour = "black", size = .5)</code></pre>
<p>I want to see if this looks similar when I use the normalized usage from leafcutter (what the QTLs actually ran on)</p>
<p>I am going to grab both the perc. and normalized.</p>
<pre class="bash"><code>
#nuclear
less /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr7.gz | grep IRF5 > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/IRF5_NuclearPropCovNorm_peaks.txt
grep IRF5 /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.phen_chr7 > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/IRF5_NuclearPropCov_peaks.txt
less /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr16.gz | grep PPP4C > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/PPP4C_NuclearPropCovNorm_peaks.txt
grep PPP4C /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.phen_chr16 > /project2/gilad/briana/threeprimeseq/data/example_gene_peakQuant/PPP4C_NuclearPropCov_peaks.txt</code></pre>
<pre class="r"><code>cov_names=c('chr', 'start', 'end', 'PeakID','NA18486' ,'NA18497', 'NA18500' ,'NA18505', 'NA18508' ,'NA18511', 'NA18519', 'NA18520', 'NA18853','NA18858', 'NA18861', 'NA18870' ,'NA18909' ,'NA18912' ,'NA18916', 'NA19092' ,'NA19093', 'NA19119', 'NA19128' ,'NA19130', 'NA19131' ,'NA19137', 'NA19140', 'NA19141' ,'NA19144', 'NA19152' ,'NA19153', 'NA19160' ,'NA19171', 'NA19193' ,'NA19200', 'NA19207', 'NA19209', 'NA19210', 'NA19223' ,'NA19225', 'NA19238' ,'NA19239', 'NA19257')
IRF5cov_all=read.table("../data/example_gene_peakQuant/IRF5_NuclearPropCov_peaks.txt", col.names = cov_names)
IRF5covNorm_all=read.table("../data/example_gene_peakQuant/IRF5_NuclearPropCovNorm_peaks.txt", col.names = cov_names)
IRF5cov_pheno=IRF5cov_all%>% select(one_of(samples))
row.names(IRF5cov_pheno)=paste("IRF5_peak", seq(1,nrow(IRF5cov_all)),sep="_")
IRF5cov_pheno= IRF5cov_pheno %>% t
IRF5cov_pheno= as.data.frame(IRF5cov_pheno) %>% rownames_to_column(var="individual")
IRF5cov_pheno_geno=IRF5cov_pheno %>% inner_join(chr7_128681297, by="individual")
IRF5cov_pheno_geno_melt= melt(IRF5cov_pheno_geno, id.vars=c("geno_dose", "individual")) %>% group_by(variable,geno_dose) %>% summarise(mean=mean(value),sd=sd(value))
IRF5cov_pheno_geno_melt$geno_dose=as.factor(IRF5cov_pheno_geno_melt$geno_dose)
irf5_covplot=ggplot(IRF5cov_pheno_geno_melt,aes(x=variable, y=mean, by=geno_dose, fill=geno_dose)) + geom_bar(stat="identity",position = "dodge") +theme(axis.text.x = element_text(angle = 90, hjust = 1)) +labs(y="Mean Coverage Prop", x="Peak", title="IRF5 peaks by chr7:128681297 genotype") + annotate("pointrange", x = 6, y = .75, ymin = .750, ymax = .750,colour = "black", size = .5)</code></pre>
<pre class="r"><code>IRF5covNorm_pheno=IRF5covNorm_all%>% select(one_of(samples))
row.names(IRF5covNorm_pheno)=paste("IRF5_peak", seq(1,nrow(IRF5covNorm_all)),sep="_")
IRF5covNorm_pheno= IRF5covNorm_pheno %>% t
IRF5covNorm_pheno= as.data.frame(IRF5covNorm_pheno) %>% rownames_to_column(var="individual")
IRF5covNorm_pheno_geno=IRF5covNorm_pheno %>% inner_join(chr7_128681297, by="individual")
IRF5covNorm_pheno_geno_melt= melt(IRF5covNorm_pheno_geno, id.vars=c("geno_dose", "individual")) %>% group_by(variable,geno_dose) %>% summarise(mean=mean(value),sd=sd(value))
IRF5covNorm_pheno_geno_melt$geno_dose=as.factor(IRF5covNorm_pheno_geno_melt$geno_dose)
irf5_normplot=ggplot(IRF5covNorm_pheno_geno_melt,aes(x=variable, y=mean, by=geno_dose, fill=geno_dose)) + geom_bar(stat="identity",position = "dodge") +theme(axis.text.x = element_text(angle = 90, hjust = 1)) +labs(y="Mean Normalized Coverage Prop", x="Peak", title="IRF5 peaks by chr7:128681297 genotype") + annotate("pointrange", x = 6, y = .75, ymin = .750, ymax = .750,colour = "black", size = .5)</code></pre>
<pre class="r"><code>pprccov_all=read.table("../data/example_gene_peakQuant/PPP4C_NuclearPropCov_peaks.txt", col.names = cov_names)
pprccovNorm_all=read.table("../data/example_gene_peakQuant/PPP4C_NuclearPropCovNorm_peaks.txt", col.names = cov_names)
pprcCov_pheno=pprccov_all%>% select(one_of(samples))
row.names(pprcCov_pheno)=paste("PPRC_peak", seq(1,nrow(pprccov_all)),sep="_")
pprcCov_pheno= pprcCov_pheno %>% t
pprcCov_pheno= as.data.frame(pprcCov_pheno) %>% rownames_to_column(var="individual")
pprcCov_pheno_geno=pprcCov_pheno %>% inner_join(chr16_30482494, by="individual")
pprcCov_pheno_geno_melt= melt(pprcCov_pheno_geno, id.vars=c("geno_dose", "individual")) %>% group_by(variable,geno_dose) %>% summarise(mean=mean(value),sd=sd(value))
pprcCov_pheno_geno_melt$geno_dose=as.factor(pprcCov_pheno_geno_melt$geno_dose)
pprc_covplot=ggplot(pprcCov_pheno_geno_melt,aes(x=variable, y=mean, by=geno_dose, fill=geno_dose)) + geom_bar(stat="identity",position = 'dodge') +theme(axis.text.x = element_text(angle = 90, hjust = 1))+ labs(y="Mean coverage proportion", x="Peak", title="PPRC peaks by 16:30482494 genotype") + annotate("pointrange", x = 2, y = .4, ymin = .4, ymax = .4,colour = "black", size = .5)</code></pre>
<pre class="r"><code>pprcCovNorm_pheno=pprccovNorm_all%>% select(one_of(samples))
row.names(pprcCovNorm_pheno)=paste("PPRC_peak", seq(1,nrow(pprccovNorm_all)),sep="_")
pprcCovNorm_pheno= pprcCovNorm_pheno %>% t
pprcCovNorm_pheno= as.data.frame(pprcCovNorm_pheno) %>% rownames_to_column(var="individual")
pprcCovNorm_pheno_geno=pprcCovNorm_pheno %>% inner_join(chr16_30482494, by="individual")
pprcCovNorm_pheno_geno_melt= melt(pprcCovNorm_pheno_geno, id.vars=c("geno_dose", "individual")) %>% group_by(variable,geno_dose) %>% summarise(mean=mean(value),sd=sd(value))
pprcCovNorm_pheno_geno_melt$geno_dose=as.factor(pprcCovNorm_pheno_geno_melt$geno_dose)
pprc_normplot=ggplot(pprcCovNorm_pheno_geno_melt,aes(x=variable, y=mean, by=geno_dose, fill=geno_dose)) + geom_bar(stat="identity",position = 'dodge') +theme(axis.text.x = element_text(angle = 90, hjust = 1))+ labs(y="Mean coverage proportion", x="Peak", title="PPRC peaks by 16:30482494 genotype") + annotate("pointrange", x = 2, y = 1, ymin = 1, ymax = 1,colour = "black", size = .5)</code></pre>
<pre class="r"><code>plot_grid(irf5_readplot,irf5_covplot,irf5_normplot)</code></pre>
<p><img src="figure/peakGeneCovEx.Rmd/unnamed-chunk-47-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>plot_grid(pprc_countplot,pprc_covplot,pprc_normplot)</code></pre>
<p><img src="figure/peakGeneCovEx.Rmd/unnamed-chunk-48-1.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 ggpubr_0.1.8
[4] magrittr_1.5 data.table_1.11.8 VennDiagram_1.6.20
[7] futile.logger_1.4.3 forcats_0.3.0 stringr_1.3.1
[10] dplyr_0.7.6 purrr_0.2.5 readr_1.1.1
[13] tidyr_0.8.1 tibble_1.4.2 ggplot2_3.0.0
[16] tidyverse_1.2.1 reshape2_1.4.3 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] lambda.r_1.2.3 modelr_0.1.2 readxl_1.1.0
[16] bindr_0.1.1 plyr_1.8.4 munsell_0.5.0
[19] gtable_0.2.0 cellranger_1.1.0 rvest_0.3.2
[22] R.methodsS3_1.7.1 evaluate_0.11 labeling_0.3
[25] knitr_1.20 broom_0.5.0 Rcpp_0.12.19
[28] formatR_1.5 backports_1.1.2 scales_1.0.0
[31] jsonlite_1.5 hms_0.4.2 digest_0.6.17
[34] stringi_1.2.4 rprojroot_1.3-2 cli_1.0.1
[37] tools_3.5.1 lazyeval_0.2.1 futile.options_1.0.1
[40] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.2
[43] xml2_1.2.0 lubridate_1.7.4 assertthat_0.2.0
[46] rmarkdown_1.10 httr_1.3.1 rstudioapi_0.8
[49] R6_2.3.0 nlme_3.1-137 git2r_0.23.0
[52] compiler_3.5.1 </code></pre>
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