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<h1 class="title toc-ignore">Evaluate Brain Data</h1>
<h4 class="author"><em>Briana Mittleman</em></h4>
<h4 class="date"><em>7/16/2018</em></h4>
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<p><strong>Last updated:</strong> 2018-07-16</p>
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Briana Mittleman
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2018-07-16
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add eval brain analysis
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<p></details></p>
<hr />
<p>I downloaded the brain 3’ seq data from <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM747470" class="uri">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM747470</a> and I want to use this analysis to see how similar their peaks are to ours eventhough the data is from different cell types.</p>
<p>First I will use the bedtools jaccard function to explore the overlaps. It will give me one stat that is the length(intersection)/length(union) - length(intersection). Here I can have file A brain peaks and file B be our peaks to see the similarity between the sets.</p>
<pre class="bash"><code>#!/bin/bash
#SBATCH --job-name=jaccard_brain
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=jacard_brain.out
#SBATCH --error=jacard_brain.err
#SBATCH --partition=broadwl
#SBATCH --mem=16G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools jaccard -a /project2/gilad/briana/threeprimeseq/data/derti_brain/GSM747470_human_brain.sites.clustered.hg19.sort.bed -b /project2/gilad/briana/threeprimeseq/data/peaks/APApeaksYL.total.bed > /project2/gilad/briana/threeprimeseq/data/derti_brain/total.jaccard.txt </code></pre>
<p>Results: intersection union-intersection jaccard n_intersections 21371 25414133 0.00084091 21352</p>
<p>The brain set has 89110 peaks and our set has 288350. I will filter ours by score then see if the top 25% have a higher overlap percentage.</p>
<pre class="r"><code>library(workflowr)</code></pre>
<pre><code>Loading required package: rmarkdown</code></pre>
<pre><code>This is workflowr version 1.0.1
Run ?workflowr for help getting started</code></pre>
<pre class="r"><code>library(dplyr)</code></pre>
<pre><code>Warning: package 'dplyr' was built under R version 3.4.4</code></pre>
<pre><code>
Attaching package: 'dplyr'</code></pre>
<pre><code>The following objects are masked from 'package:stats':
filter, lag</code></pre>
<pre><code>The following objects are masked from 'package:base':
intersect, setdiff, setequal, union</code></pre>
<pre class="r"><code>library(tidyr)
library(ggplot2)
library(reshape2)</code></pre>
<pre><code>Warning: package 'reshape2' was built under R version 3.4.3</code></pre>
<pre><code>
Attaching package: 'reshape2'</code></pre>
<pre><code>The following object is masked from 'package:tidyr':
smiths</code></pre>
<pre class="r"><code>YL_peaks=read.table("../data/bedgraph_peaks/APApeaks.bed", col.names = c("chr", "start", "end", "count", "strand", "score")) %>% mutate(length=end-start)</code></pre>
<pre><code>Warning: package 'bindrcpp' was built under R version 3.4.4</code></pre>
<p>I want the counts for the top 25% of the peaks.</p>
<pre class="r"><code>quantile(YL_peaks$count)</code></pre>
<pre><code> 0% 25% 50% 75% 100%
1.000000e+00 1.343902e+01 2.353933e+01 6.091061e+01 1.604636e+06 </code></pre>
<p>I will subset the peaks by having a count > 61.</p>
<pre class="bash"><code>awk '$4 >= 60 {print}' APApeaksYL.total.bed > APApeaksYL.top25.total.bed</code></pre>
<p>I can rerun the jaccard with this and see if it changes, this new file has 72877 peaks.</p>
<p>Results:<br />
intersection union-intersection jaccard n_intersections 13221 6452066 0.00204911 13207</p>
<p>The proportion of overlap increased. Next I can try to make plots where I seperate my peaks by if they have a corresponding one in the brain file then plot the scores. To do this I will first use bedtool intersect to get just my peaks that contain a peak in the brain file. I can then use dplyr to merge them.</p>
<p>Here A is my file and B is the brain file.</p>
<pre class="bash"><code>#!/bin/bash
#SBATCH --job-name=int_brain
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=int.brain.out
#SBATCH --error=int.brain.err
#SBATCH --partition=broadwl
#SBATCH --mem=16G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools intersect -wa -a /project2/gilad/briana/threeprimeseq/data/peaks/APApeaksYL.total.bed -b /project2/gilad/briana/threeprimeseq/data/derti_brain/GSM747470_human_brain.sites.clustered.hg19.sort.bed > /project2/gilad/briana/threeprimeseq/data/derti_brain/APApeaksYL.total.inbrain.bed
</code></pre>
<p>The resulting file has 21378 peaks.</p>
<pre class="r"><code>YL_peaks_overlap=read.table("../data/APApeaksYL.total.inbrain.bed", col.names = c("chr", "start", "end", "count", "strand", "score")) %>% mutate(length=end-start) %>% mutate(in_brain="Y")</code></pre>
<p>Now I need to join them.</p>
<pre class="r"><code>YL_peaks_join=YL_peaks %>% full_join(YL_peaks_overlap, by = c("chr", "start", "end", "count", "strand", "score", "length"))
YL_peaks_join$in_brain[is.na(YL_peaks_join$in_brain)]="N"
YL_peaks_join_sel=YL_peaks_join %>% select(count, in_brain)</code></pre>
<p>Plot these.</p>
<pre class="r"><code>ggplot(YL_peaks_join_sel, aes(y=log10(count), x=in_brain, fill=in_brain)) + geom_boxplot() + labs(x="Peak called in brain dataset", y="log10 Score", title="Peak score distribution by inclusion in brain dataset")</code></pre>
<p><img src="figure/eval.brain.Rmd/unnamed-chunk-9-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>ggplot(YL_peaks_join_sel, aes(x=log10(count), fill=in_brain), bins=50) + geom_density(position="identity", alpha=.5) + labs(x="log10 of Score", title="Distribution of log10 Scores in peaks included in brain dataset")</code></pre>
<p><img src="figure/eval.brain.Rmd/unnamed-chunk-10-1.png" width="672" style="display: block; margin: auto;" /></p>
<p>It would be better if the background was just a random subset of the same number. There are 21378 included peaks so I should select a random 21378 to make a background distribution.</p>
<pre class="r"><code>samp_YLpeaks= sample_n(YL_peaks, 21378)
ggplot() + geom_histogram(data=samp_YLpeaks, aes(log10(count)), bins=100) + geom_histogram(data=YL_peaks_overlap, aes(log10(count)),fill="Red", bins=100) + labs(x="Log10 of Score", title="Scores in Overlapping set compared to scores in random set")</code></pre>
<p><img src="figure/eval.brain.Rmd/unnamed-chunk-11-1.png" width="672" style="display: block; margin: auto;" /></p>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>R version 3.4.2 (2017-09-28)
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/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/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 ggplot2_2.2.1 tidyr_0.7.2
[5] dplyr_0.7.5 workflowr_1.0.1 rmarkdown_1.8.5
loaded via a namespace (and not attached):
[1] Rcpp_0.12.17 compiler_3.4.2 pillar_1.1.0
[4] git2r_0.21.0 plyr_1.8.4 bindr_0.1.1
[7] R.methodsS3_1.7.1 R.utils_2.6.0 tools_3.4.2
[10] digest_0.6.15 evaluate_0.10.1 tibble_1.4.2
[13] gtable_0.2.0 pkgconfig_2.0.1 rlang_0.2.1
[16] yaml_2.1.19 stringr_1.3.1 knitr_1.18
[19] rprojroot_1.3-2 grid_3.4.2 tidyselect_0.2.4
[22] glue_1.2.0 R6_2.2.2 purrr_0.2.5
[25] magrittr_1.5 whisker_0.3-2 backports_1.1.2
[28] scales_0.5.0 htmltools_0.3.6 assertthat_0.2.0
[31] colorspace_1.3-2 labeling_0.3 stringi_1.2.2
[34] lazyeval_0.2.1 munsell_0.4.3 R.oo_1.22.0 </code></pre>
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