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<h1 class="title toc-ignore">28 Ind. Peak Quant</h1>
<h4 class="author"><em>Briana Mittleman</em></h4>
<h4 class="date"><em>8/9/2018</em></h4>
</div>
<p><strong>Last updated:</strong> 2018-08-13</p>
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<pre><code>
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start peak explore analysis for 54 libraries
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<p></details></p>
<hr />
<p>I know have 28 individuals sequences on 2 lanes. I have combined these and used the peak coverage pipeline to call and clean peaks. I will use this analysis to explore the library sizes and coverage at these peaks.</p>
<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>
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Attaching package: 'cowplot'</code></pre>
<pre><code>The following object is masked from 'package:ggplot2':
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<pre class="r"><code>library(reshape2)</code></pre>
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Attaching package: 'reshape2'</code></pre>
<pre><code>The following object is masked from 'package:tidyr':
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<pre class="r"><code>library(devtools)</code></pre>
<div id="reads-and-mapping-stats" class="section level2">
<h2>Reads and Mapping Stats:</h2>
<pre class="r"><code>map_stats=read.csv("../data/comb_map_stats.csv", header=T)
map_stats$line=as.factor(map_stats$line)
map_stats$batch=as.factor(map_stats$batch)</code></pre>
<p>The number of reads for each library and the number of mapped reads.</p>
<pre class="r"><code>read_plot=ggplot(map_stats, aes(x=line, y=comb_reads, fill=fraction))+ geom_bar(stat="identity", position="dodge") +labs(y="Reads", title="Reads by line and fraction")
map_plot=ggplot(map_stats, aes(x=line, y=comb_mapped, fill=fraction))+ geom_bar(stat="identity", position="dodge") +labs(y="Mapped Reads", title="Mapped reads by line and fraction") + geom_hline(yintercept=10000000) + annotate("text",label="10 million mapped reads", y=9000000, x=10)
plot_grid(read_plot, map_plot)</code></pre>
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<p></details></p>
<p>The percent of reads that map per line are pretty uniform accross libraries. The mean is 72%.</p>
<pre class="r"><code>ggplot(map_stats, aes(x=line, y=comb_prop_mapped, fill=fraction))+ geom_bar(stat="identity", position="dodge") +labs(y="Mapped Percent", title="Percent of reads mapping by line and fraction") </code></pre>
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<p></details></p>
<pre class="r"><code>mean(map_stats$comb_prop_mapped)</code></pre>
<pre><code>[1] 0.7230478</code></pre>
</div>
<div id="clean-peak-exploration" class="section level2">
<h2>Clean peak exploration</h2>
<pre class="r"><code>peak_quant=read.table(file = "../data/clean_peaks/APAquant.fc.cleanpeaks.fc", header=T)</code></pre>
<p>Fix the names</p>
<pre class="r"><code>file_names=colnames(peak_quant)[7:62]
file_names_split=lapply(file_names, function(x)strsplit(x,".", fixed=T))
libraries=c()
for (i in file_names_split){
unlist_i=unlist(i)
libraries=c(libraries, paste(unlist_i[10], unlist_i[11], sep="-"))
}
colnames(peak_quant)=c(colnames(peak_quant)[1:6], libraries) </code></pre>
<p>Explore the peaks before quantifications:</p>
<pre class="r"><code>#length of peaks
plot(sort(peak_quant$Length,decreasing = T), main="Peak Lengths", ylab="Peak Length", xlab="Peak index")</code></pre>
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<p></details></p>
<pre class="r"><code>#mean cov of peaks
peak_cov=peak_quant %>% select(contains("-"))
peak_mean=apply(peak_cov,1,mean)
peak_var=apply(peak_cov, 1, var)
plot(log10(sort(peak_mean,decreasing = T)))</code></pre>
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<p></details></p>
<pre class="r"><code>plot(peak_var)</code></pre>
<p><img src="figure/28ind.peak.explore.Rmd/unnamed-chunk-7-3.png" width="672" style="display: block; margin: auto;" /></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/38bfbaf6b3e1b6f8e8fd397af1b86d34dd711a9e/docs/figure/28ind.peak.explore.Rmd/unnamed-chunk-7-3.png" target="_blank">38bfbaf</a>
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<p></details></p>
<pre class="r"><code>plot(log10(peak_var)~log10(peak_mean))</code></pre>
<p><img src="figure/28ind.peak.explore.Rmd/unnamed-chunk-7-4.png" width="672" style="display: block; margin: auto;" /></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/38bfbaf6b3e1b6f8e8fd397af1b86d34dd711a9e/docs/figure/28ind.peak.explore.Rmd/unnamed-chunk-7-4.png" target="_blank">38bfbaf</a>
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brimittleman
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<p></details></p>
<p>Plot the coverage vs the length:</p>
<pre class="r"><code>plot(peak_mean~peak_quant$Length)</code></pre>
<p><img src="figure/28ind.peak.explore.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/38bfbaf6b3e1b6f8e8fd397af1b86d34dd711a9e/docs/figure/28ind.peak.explore.Rmd/unnamed-chunk-8-1.png" target="_blank">38bfbaf</a>
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brimittleman
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2018-08-09
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<p></details></p>
</div>
<div id="clustering" class="section level2">
<h2>Clustering:</h2>
<p>CLustering:</p>
<pre class="r"><code>pca_peak= prcomp(peak_cov,center = TRUE,scale. = TRUE)
summary(pca_peak)</code></pre>
<pre><code>Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6 PC7
Standard deviation 6.5936 2.7291 1.66983 0.71519 0.6392 0.5651 0.52807
Proportion of Variance 0.7763 0.1330 0.04979 0.00913 0.0073 0.0057 0.00498
Cumulative Proportion 0.7763 0.9093 0.95915 0.96828 0.9756 0.9813 0.98626
PC8 PC9 PC10 PC11 PC12 PC13
Standard deviation 0.35808 0.29502 0.25231 0.23732 0.22343 0.19515
Proportion of Variance 0.00229 0.00155 0.00114 0.00101 0.00089 0.00068
Cumulative Proportion 0.98855 0.99010 0.99124 0.99224 0.99314 0.99382
PC14 PC15 PC16 PC17 PC18 PC19
Standard deviation 0.1832 0.17407 0.16032 0.14346 0.14122 0.13574
Proportion of Variance 0.0006 0.00054 0.00046 0.00037 0.00036 0.00033
Cumulative Proportion 0.9944 0.99496 0.99542 0.99578 0.99614 0.99647
PC20 PC21 PC22 PC23 PC24 PC25
Standard deviation 0.1300 0.12760 0.11966 0.11231 0.10982 0.10799
Proportion of Variance 0.0003 0.00029 0.00026 0.00023 0.00022 0.00021
Cumulative Proportion 0.9968 0.99706 0.99732 0.99754 0.99776 0.99796
PC26 PC27 PC28 PC29 PC30 PC31
Standard deviation 0.10167 0.09704 0.09243 0.08607 0.08012 0.07887
Proportion of Variance 0.00018 0.00017 0.00015 0.00013 0.00011 0.00011
Cumulative Proportion 0.99815 0.99832 0.99847 0.99860 0.99872 0.99883
PC32 PC33 PC34 PC35 PC36 PC37
Standard deviation 0.07689 0.07601 0.07153 0.06993 0.06656 0.06334
Proportion of Variance 0.00011 0.00010 0.00009 0.00009 0.00008 0.00007
Cumulative Proportion 0.99893 0.99904 0.99913 0.99922 0.99929 0.99937
PC38 PC39 PC40 PC41 PC42 PC43
Standard deviation 0.06152 0.05822 0.05643 0.05371 0.05236 0.04715
Proportion of Variance 0.00007 0.00006 0.00006 0.00005 0.00005 0.00004
Cumulative Proportion 0.99943 0.99949 0.99955 0.99960 0.99965 0.99969
PC44 PC45 PC46 PC47 PC48 PC49
Standard deviation 0.04656 0.04584 0.04252 0.04214 0.03873 0.03696
Proportion of Variance 0.00004 0.00004 0.00003 0.00003 0.00003 0.00002
Cumulative Proportion 0.99973 0.99977 0.99980 0.99983 0.99986 0.99988
PC50 PC51 PC52 PC53 PC54 PC55
Standard deviation 0.03557 0.03351 0.03191 0.03020 0.02938 0.02733
Proportion of Variance 0.00002 0.00002 0.00002 0.00002 0.00002 0.00001
Cumulative Proportion 0.99991 0.99993 0.99994 0.99996 0.99998 0.99999
PC56
Standard deviation 0.02525
Proportion of Variance 0.00001
Cumulative Proportion 1.00000</code></pre>
<pre class="r"><code>pc_df=as.data.frame(pca_peak$rotation) %>% rownames_to_column(var="lib") %>% mutate(fraction=ifelse(grepl("T", lib), "total", "nuclear"))
ggplot(pc_df, aes(x=PC1, y=PC2, col=fraction)) + geom_point() + labs(x="PC1: Prop explained 0.7763", y="PC2: Prop explained 0.1330", title="Raw PAS qunatification data")</code></pre>
<p><img src="figure/28ind.peak.explore.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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<td style="text-align:left;">
brimittleman
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<td style="text-align:left;">
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<a href="https://github.com/brimittleman/threeprimeseq/blob/38bfbaf6b3e1b6f8e8fd397af1b86d34dd711a9e/docs/figure/28ind.peak.explore.Rmd/unnamed-chunk-10-1.png" target="_blank">38bfbaf</a>
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brimittleman
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<p></details></p>
<p>I now want to explore what the first PC is representing. Some ideas are:</p>
<ul>
<li><p>batch</p></li>
<li><p>sequencing depth</p></li>
<li><p>mapped reads</p></li>
</ul>
<p>All of this info is in the map stats.</p>
<pre class="r"><code>pc_df=as.data.frame(pca_peak$rotation) %>% rownames_to_column(var="lib") %>% mutate(fraction=ifelse(grepl("T", lib), "total", "nuclear")) %>% mutate(reads=map_stats$comb_reads) %>% mutate(batch=map_stats$batch) %>% mutate(mapped=map_stats$comb_mapped)
batch_gg= ggplot(pc_df, aes(x=PC1, y=PC2, col=batch)) + geom_point() + labs(x="PC1: Prop explained 0.7763", y="PC2: Prop explained 0.1330", title="Batch")
frac_gg= ggplot(pc_df, aes(x=PC1, y=PC2, col=fraction)) + geom_point() + labs(x="PC1: Prop explained 0.7763", y="PC2: Prop explained 0.1330", title="Fraction")
reads_gg= ggplot(pc_df, aes(x=PC1, y=PC2, col=reads)) + geom_point() + labs(x="PC1: Prop explained 0.7763", y="PC2: Prop explained 0.1330", title="Reads")
mapped_gg= ggplot(pc_df, aes(x=PC1, y=PC2, col=mapped)) + geom_point() + labs(x="PC1: Prop explained 0.7763", y="PC2: Prop explained 0.1330", title="Mapped Reads")
plot_grid(frac_gg,batch_gg,reads_gg,mapped_gg)</code></pre>
<p><img src="figure/28ind.peak.explore.Rmd/unnamed-chunk-11-1.png" width="672" style="display: block; margin: auto;" /></p>
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brimittleman
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<p></details></p>
<p>Proportion of reads mapping to peaks. This may be in the feature counts summary.</p>
<pre class="r"><code>map_summary=read.table("../data/clean_peaks/APAquant.fc.cleanpeaks.fc.summary", header=T)
colnames(map_summary)=c(colnames(map_summary)[1], libraries)
map_summary_m=map_summary[,2:57] %>% t
colnames(map_summary_m)=map_summary$Status
map_summary_m_df= as.data.frame(map_summary_m) %>% mutate(perc_feature=(Assigned/(Assigned+Unassigned_NoFeatures)))</code></pre>
<pre class="r"><code>pc_df_mapsum=as.data.frame(pca_peak$rotation) %>% rownames_to_column(var="lib") %>% mutate(fraction=ifelse(grepl("T", lib), "total", "nuclear")) %>% mutate(reads=map_stats$comb_reads) %>% mutate(batch=map_stats$batch) %>% mutate(mapped=map_stats$comb_mapped) %>% mutate(PercFeature=map_summary_m_df$perc_feature)
propfrac_gg= ggplot(pc_df_mapsum, aes(x=PC1, y=PC2, col=PercFeature)) + geom_point() + labs(x="PC1: Prop explained 0.7763", y="PC2: Prop explained 0.1330", title="Percent Reads mapping to a feature")
propfrac_gg</code></pre>
<p><img src="figure/28ind.peak.explore.Rmd/unnamed-chunk-13-1.png" width="672" style="display: block; margin: auto;" /></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/2e06a709381efd1768bf354c4a300161b58c55f0/docs/figure/28ind.peak.explore.Rmd/unnamed-chunk-13-1.png" target="_blank">2e06a70</a>
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brimittleman
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<td style="text-align:left;">
2018-08-13
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<a href="https://github.com/brimittleman/threeprimeseq/blob/38bfbaf6b3e1b6f8e8fd397af1b86d34dd711a9e/docs/figure/28ind.peak.explore.Rmd/unnamed-chunk-13-1.png" target="_blank">38bfbaf</a>
</td>
<td style="text-align:left;">
brimittleman
</td>
<td style="text-align:left;">
2018-08-09
</td>
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</table>
<p></details></p>
</div>
<div id="map-peaks-to-genes" class="section level2">
<h2>Map peaks to genes</h2>
<p>I want to use the bedtools closest command to find the clostest protein coding gene to each peak.</p>
<ul>
<li><p>-s (require same strandedness)</p></li>
<li><p>-d add a column with distance to closest (strand spec) report distance wrt the genes</p></li>
<li><p>A is the peak</p></li>
<li><p>B is protein coding genes</p></li>
</ul>
<pre class="bash"><code>#!/bin/bash
#SBATCH --job-name=mapgene2peak
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mapgene2peak.out
#SBATCH --error=mapgene2peak.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools closest -s -d -a /project2/gilad/briana/threeprimeseq/data/clean.peaks_comb/APApeaks_combined_clean_fixed.bed -b /project2/gilad/briana/genome_anotation_data/gencode.v19.annotation.proteincodinggene.bed > /project2/gilad/briana/threeprimeseq/data/clean.peaks_comb/APApeaks_comb_clean_dist2gene.txt</code></pre>
<pre class="r"><code>clean_peaks=read.table("../data/clean_peaks/APApeaks_combined_clean.bed", header=F)</code></pre>
<p>This scirpt is called fix_cleanpeakbed.py</p>
<pre class="bash"><code>from misc_helper import *
fout = file("/project2/gilad/briana/threeprimeseq/data/clean.peaks_comb/APApeaks_combined_clean_fixed.bed",'w')
for ln in open("/project2/gilad/briana/threeprimeseq/data/clean.peaks_comb/APApeaks_combined_clean.bed"):
chrom, start, end, name, cov, strand, score2 = ln.split()
chrom_nochr= int(chrom[3:])
start_i = int(start)
end_i = int(end)
cov_i=float(cov)
fout.write("%d\t%d\t%d\t%s\t%f\t%s\n"%(chrom_nochr, start_i, end_i, name, cov_i, strand))
fout.close()
</code></pre>
<p>Second option is to use bedtools map. This will tell me directly how many peaks overlap each gene. I will use the -c and -o commands. I want it to tell me the number of peaks rather than the mean of the coverage. I can give it the name column as the -c and -0 count_distinct</p>
<pre class="bash"><code>#!/bin/bash
#SBATCH --job-name=mapgene2peak2
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mapgene2peak2.out
#SBATCH --error=mapgene2peak2.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools map -c 4 -o count_distinct -a /project2/gilad/briana/genome_anotation_data/gencode.v19.annotation.proteincodinggene.bed -b /project2/gilad/briana/threeprimeseq/data/clean.peaks_comb/APApeaks_combined_clean_fixed.bed > /project2/gilad/briana/threeprimeseq/data/clean.peaks_comb/APApeaks_combined_clean_countdistgenes.txt</code></pre>
<p>First look at the count for peaks in genes. this will not give us information for peaks outside of the annotaiton.</p>
<pre class="r"><code>genes_per_peak=read.table("../data/clean_peaks/APApeaks_combined_clean_countdistgenes.txt", header=F, stringsAsFactors = F, col.names = c("chr", "strart", "end", "gene", "score", "strand", "numPeaks"))
nrow(genes_per_peak)</code></pre>
<pre><code>[1] 20345</code></pre>
<pre class="r"><code>genes_with_peak= genes_per_peak %>% filter(numPeaks>0) %>% nrow()
genes_with_peak</code></pre>
<pre><code>[1] 12867</code></pre>
<pre class="r"><code>genes_with_2peak= genes_per_peak %>% filter(numPeaks>1) %>% nrow()
genes_with_2peak</code></pre>
<pre><code>[1] 10797</code></pre>
<p>There are in total 20,345 genes in the protein coding gene set. There are 12,867 with at least 1 peak and 10,797 with at least 2 peaks.</p>
<p>Look at the distribution:</p>
<pre class="r"><code>plot(sort(genes_per_peak$numPeaks, decreasing = T), ylab="Number of Peaks", main="Number of peaks mapping to each protein coding gene")</code></pre>
<p><img src="figure/28ind.peak.explore.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/2e06a709381efd1768bf354c4a300161b58c55f0/docs/figure/28ind.peak.explore.Rmd/unnamed-chunk-19-1.png" target="_blank">2e06a70</a>
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<p></details></p>
<p>The next dataset has more information aboutthe distance to the gene. If it overlaps, the distance should be 0.</p>
<pre class="r"><code>dist_to_peak=read.table("../data/clean_peaks/APApeaks_comb_clean_dist2gene.txt", header=F)</code></pre>
</div>
<div id="compare-this-to-rnaseq" class="section level2">
<h2>Compare this to RNAseq</h2>
<pre class="r"><code>rnaseq_18486=read.table("../data/18486.genecov.txt", header=F,col.names = c("chr", "start", "end", "name", "score", "strand", "count"))
expressed_genes=rnaseq_18486 %>% filter(count>0) %>% nrow()
expressed_genes</code></pre>
<pre><code>[1] 16925</code></pre>
<p>Get gene level coverage for 18486T 3’ seq data. I have this in /project2/gilad/briana/threeprimeseq/data/gene_cov.</p>
<pre class="r"><code>threeprime_18486T=read.table("../data/YL-SP-18486-T-combined-genecov.txt", header=F, col.names = c("chr", "start", "end", "name", "score", "strand", "count"))</code></pre>
<pre class="r"><code>rnaseq_sm=rnaseq_18486 %>% select("name", "count")
threeprime_sm=threeprime_18486T %>% select("name", "count")
gene_cov=rnaseq_sm %>% left_join(threeprime_sm, by= "name")
names(gene_cov)= c("name", "rnaseq", "threeprime")
ggplot(gene_cov,aes(x=log10(rnaseq), y=log10(threeprime)))+ geom_point(na.rm=TRUE, size=.1) + geom_density2d(na.rm = TRUE, size = 1, colour = 'red') + labs(y='Log(three prime seq gene Count)', x='Log(RNA seq gene Count)', title="Correlation between three prime seq and rna seq read counts") + xlab('Log(RNA seq gene Count)') + geom_smooth(method="lm")</code></pre>
<pre><code>Warning: Removed 6778 rows containing non-finite values (stat_smooth).</code></pre>
<p><img src="figure/28ind.peak.explore.Rmd/unnamed-chunk-23-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-23-1.png:</em></summary>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/2e06a709381efd1768bf354c4a300161b58c55f0/docs/figure/28ind.peak.explore.Rmd/unnamed-chunk-23-1.png" target="_blank">2e06a70</a>
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<td style="text-align:left;">
brimittleman
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<td style="text-align:left;">
2018-08-13
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<p></details></p>
<p>Subset genes with coverage in RNA seq but not in three prime seq.</p>
<pre class="r"><code>genecov_onlyRNA=gene_cov %>% filter(threeprime==0 & rnaseq !=0) %>% arrange(desc(rnaseq))</code></pre>
<p>What I actually want to look at genes with no peaks but RNA reads.</p>
<pre class="r"><code>colnames(rnaseq_sm)=c("gene", "RNAseqCount")
cov_rnavthreeprime= rnaseq_sm %>% left_join(genes_per_peak, by="gene") %>% select(gene, RNAseqCount, numPeaks)</code></pre>
<pre><code>Warning: Column `gene` joining factor and character vector, coercing into
character vector</code></pre>
<pre class="r"><code>ggplot(cov_rnavthreeprime,aes(x=log10(RNAseqCount), y=log10(numPeaks)))+ geom_point(na.rm=TRUE, size=.1) + geom_density2d(na.rm = TRUE, size = 1, colour = 'red') + geom_smooth(method="lm")</code></pre>
<pre><code>Warning: Removed 7575 rows containing non-finite values (stat_smooth).</code></pre>
<p><img src="figure/28ind.peak.explore.Rmd/unnamed-chunk-25-1.png" width="672" style="display: block; margin: auto;" /></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/2e06a709381efd1768bf354c4a300161b58c55f0/docs/figure/28ind.peak.explore.Rmd/unnamed-chunk-25-1.png" target="_blank">2e06a70</a>
</td>
<td style="text-align:left;">
brimittleman
</td>
<td style="text-align:left;">
2018-08-13
</td>
</tr>
</tbody>
</table>
<p></details></p>
<p>Explore how many genes have coverage in the RNAseq set but not the</p>
<pre class="r"><code>NotInThreePrime=cov_rnavthreeprime %>% filter(numPeaks==0 & RNAseqCount !=0) %>% arrange(desc(RNAseqCount))</code></pre>
<pre class="r"><code>par(mfrow=c(1,2))
plot(log10(sort(cov_rnavthreeprime$RNAseqCount, decreasing = T)))
plot(log10(NotInThreePrime$RNAseqCount), main="Post-clean up, coverage of RNA seq without a PAS", xlab="gene", ylab="log10 coverage RNA seq 18486")</code></pre>
<p><img src="figure/28ind.peak.explore.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/2e06a709381efd1768bf354c4a300161b58c55f0/docs/figure/28ind.peak.explore.Rmd/unnamed-chunk-27-1.png" target="_blank">2e06a70</a>
</td>
<td style="text-align:left;">
brimittleman
</td>
<td style="text-align:left;">
2018-08-13
</td>
</tr>
</tbody>
</table>
<p></details></p>
<p>Top RNA seq without peaks:</p>
<ul>
<li><p>GAPDH - doesnt male sense. Visually see peak</p></li>
<li><p>EEF2</p></li>
<li><p>PFN1</p></li>
<li><p>PTMA</p></li>
</ul>
<div id="try-with-peaks-before-cleaning" class="section level3">
<h3>Try with peaks before cleaning</h3>
<p>In looking at IGV the places have coverage and peaks are called in Yang protocol but they are all removed in the cleaning step. I want to look at how many genes have APA when we use the pre-cleaned list of peaks. This file is filtered_APApeaks_merged_allchrom.bed. I need to map these to genes like I did the other peaks.</p>
<p>This scirpt is called fix_dirtypeakbed.py</p>
<pre class="bash"><code>from misc_helper import *
fout = file("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed",'w')
for ln in open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.bed"):
chrom, start, end, name, cov, strand, score2 = ln.split()
chrom_nochr= int(chrom[3:])
start_i = int(start)
end_i = int(end)
cov_i=float(cov)
fout.write("%d\t%d\t%d\t%s\t%f\t%s\n"%(chrom_nochr, start_i, end_i, name, cov_i, strand))
fout.close()
</code></pre>
<p>mapgene2peak_dirty.sh</p>
<pre class="bash"><code>#!/bin/bash
#SBATCH --job-name=mapgene2peak_dirty
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mapgene2peak_dirty.out
#SBATCH --error=mapgene2peak_dirty.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools closest -s -d -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed -b /project2/gilad/briana/genome_anotation_data/gencode.v19.annotation.proteincodinggene.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_dist2gene.txt</code></pre>
<pre class="bash"><code>#!/bin/bash
#SBATCH --job-name=mapgene2peak2_dirty
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mapgene2peak2_dirty.out
#SBATCH --error=mapgene2peak2_dirty.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools map -c 4 -o count_distinct -a /project2/gilad/briana/genome_anotation_data/gencode.v19.annotation.proteincodinggene.bed -b /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_countdistgenes.txt</code></pre>
<pre class="r"><code>dirty_peak=read.table("../data/clean_peaks/filtered_APApeaks_merged_allchrom_countdistgenes.txt", col.names = c("chr", "start", "end", "gene", "score", "strand", "numPeaks"))
genes_with_peak_dirty= dirty_peak %>% filter(numPeaks>0) %>% nrow()
genes_with_peak_dirty</code></pre>
<pre><code>[1] 14678</code></pre>
<pre class="r"><code>genes_with_2peak_dirty= dirty_peak %>% filter(numPeaks>1) %>% nrow()
genes_with_2peak_dirty</code></pre>
<pre><code>[1] 12761</code></pre>
<pre class="r"><code>rnaseq_dirtypeaks=rnaseq_sm %>% left_join(dirty_peak, by="gene") %>% mutate(length=end-start) %>% select(gene, RNAseqCount, numPeaks, length, start, end, chr)
dirty_onlyinRNA= rnaseq_dirtypeaks %>% filter(numPeaks==0 & RNAseqCount !=0) %>% arrange(desc(RNAseqCount))
summary(rnaseq_dirtypeaks$length)</code></pre>
<pre><code> Min. 1st Qu. Median Mean 3rd Qu. Max.
59 8547 25654 65309 67508 2304638 </code></pre>
<pre class="r"><code>summary(dirty_onlyinRNA$length)</code></pre>
<pre><code> Min. 1st Qu. Median Mean 3rd Qu. Max.
59 4500 16166 51003 53592 1117544 </code></pre>
<pre class="r"><code>par(mfrow=c(1,2))
plot(log10(dirty_onlyinRNA$RNAseqCount), main="Pre-clean up, \n coverage of RNA seq without a PAS", xlab="gene", ylab="log10 coverage RNA seq 18486")
plot(log10(NotInThreePrime$RNAseqCount), main="Post-clean up, \n coverage of RNA seq without a PAS", xlab="gene", ylab="log10 coverage RNA seq 18486")</code></pre>
<p><img src="figure/28ind.peak.explore.Rmd/unnamed-chunk-33-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-33-1.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/2e06a709381efd1768bf354c4a300161b58c55f0/docs/figure/28ind.peak.explore.Rmd/unnamed-chunk-33-1.png" target="_blank">2e06a70</a>
</td>
<td style="text-align:left;">
brimittleman
</td>
<td style="text-align:left;">
2018-08-13
</td>
</tr>
</tbody>
</table>
<p></details></p>
</div>
</div>
<div id="map-to-refseq" class="section level2">
<h2>Map to refseq</h2>
<p>It looks like mapping to the refseq genes may be more effective here due to the better 3’ UTR annotatations</p>
<pre class="bash"><code>#!/bin/bash
#SBATCH --job-name=mapgene2peak2_dirty_refseq
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mapgene2peak2_dirty_rs.out
#SBATCH --error=mapgene2peak2_dirty_rs.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools map -c 4 -o count_distinct -a /project2/gilad/briana/genome_anotation_data/ncbiRefSeq_sm_noChr.sort.bed -b /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseq_countdistgenes.txt
</code></pre>
<p>Also do coverage for the RNA seq with this.</p>
<pre class="bash"><code>#!/bin/bash
#SBATCH --job-name=refseq-rnaseqcov18486
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=refseq-rnaseqcov18486.out
#SBATCH --error=refseq-rnaseqcov18486.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools coverage -counts -sorted -a /project2/gilad/briana/genome_anotation_data/ncbiRefSeq_sm.sort.bed-b /project2/gilad/briana/threeprimeseq/data/rnaseq_bed/18486.bed > /project2/gilad/briana/threeprimeseq/data/rnaseq_cov/18486.refseq.gencov.txt</code></pre>
<p>bedfile is currupt. i need to make one from teh original vcf.</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 devtools_1.13.6 reshape2_1.4.3 cowplot_0.9.3
[5] workflowr_1.1.1 forcats_0.3.0 stringr_1.3.1 dplyr_0.7.6
[9] purrr_0.2.5 readr_1.1.1 tidyr_0.8.1 tibble_1.4.2
[13] ggplot2_3.0.0 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_2.1.19
[7] rlang_0.2.1 R.oo_1.22.0 pillar_1.3.0
[10] glue_1.3.0 withr_2.1.2 R.utils_2.6.0
[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] memoise_1.1.0 evaluate_0.11 labeling_0.3
[25] knitr_1.20 broom_0.5.0 Rcpp_0.12.18
[28] backports_1.1.2 scales_0.5.0 jsonlite_1.5
[31] hms_0.4.2 digest_0.6.15 stringi_1.2.4
[34] grid_3.5.1 rprojroot_1.3-2 cli_1.0.0
[37] tools_3.5.1 magrittr_1.5 lazyeval_0.2.1
[40] crayon_1.3.4 whisker_0.3-2 pkgconfig_2.0.1
[43] MASS_7.3-50 xml2_1.2.0 lubridate_1.7.4
[46] assertthat_0.2.0 rmarkdown_1.10 httr_1.3.1
[49] rstudioapi_0.7 R6_2.2.2 nlme_3.1-137
[52] git2r_0.23.0 compiler_3.5.1 </code></pre>
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