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<h1 class="title toc-ignore">Differential isoform usage netween total and nuclear fractions</h1>
<h4 class="author"><em>Briana Mittleman</em></h4>
<h4 class="date"><em>6/5/2018</em></h4>
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<p><strong>Last updated:</strong> 2018-06-06</p>
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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:
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add plots for pos effect size
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change plots to ratio of reads in gene
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plotting diff isoform hits
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2018-06-06
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start dif isoform analysis
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<p></details></p>
<hr />
<p>In this analysis I will use the file I created in the previous analysis along with the leafcutter software to run a differential isoform usage analysis between my total and nucelar fractions.</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(ggplot2)
library(tidyr)
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(edgeR)</code></pre>
<pre><code>Warning: package 'edgeR' was built under R version 3.4.3</code></pre>
<pre><code>Loading required package: limma</code></pre>
<pre><code>Warning: package 'limma' was built under R version 3.4.3</code></pre>
<pre class="r"><code>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>
<div id="final-data-preparation" class="section level2">
<h2>Final data preparation:</h2>
<p>Create the differential sample file. It will have the names of the samples in column 1 and the fraction they belong to in column two.</p>
<pre class="r"><code>isoform_data=read.table("../data/leafcutter/all_leaf_200wind.csv")
samples=colnames(isoform_data)
fraction=c()
for (i in samples){
if(grepl("N", i)){
fraction=c(fraction, "Nuclear")
}
else{
fraction=c(fraction, "Total")
}
}
sample_anno=cbind(samples,fraction)</code></pre>
<p>I will write this to the leafcutter directory without the header.</p>
<pre class="r"><code>#write.table(sample_anno, file="../data/leafcutter/sample_ano.txt", row.names = FALSE, quote = FALSE, sep=" ", col.names = F)</code></pre>
</div>
<div id="leafcutter-results" class="section level2">
<h2>Leafcutter results</h2>
<p>Confirm we only have 2188 genes with APA here.</p>
<pre class="r"><code>genes.anno=data.frame(x=rownames(isoform_data)) %>% separate(col=x, into=c("chr","bin","gene"), sep=":")
n_genes= n_distinct(genes.anno$gene)
num_gene=genes.anno %>% group_by(gene) %>% select(gene) %>% tally() %>% filter(n>1)</code></pre>
<pre><code>Warning: package 'bindrcpp' was built under R version 3.4.4</code></pre>
<pre class="r"><code>dim(num_gene)</code></pre>
<pre><code>[1] 2188 2</code></pre>
<p>We have 3797 unique genes in this file and only 2188 have multiple bins passing the filter.</p>
<p>I ran leafcutter on the cluster with the following command.</p>
<pre class="bash"><code>Rscript /project2/gilad/briana/leafcutter/scripts/leafcutter_ds.R all_apa_perind.csv.gz sample_ano.txt -o APA</code></pre>
<p>The resutls for significant bins are in the effet size file.</p>
<pre class="r"><code>effect_size=read.table("../data/leafcutter/APA_effect_sizes.txt", header=T)
effect_size= effect_size %>% separate(col=intron, into=c("chr","start","end", "gene"), sep=":")
effect_size= effect_size %>% separate(col=gene, into=c("clu", "gene", "strand"), sep="_")</code></pre>
<pre class="r"><code>counts=read.table("../data/leafcutter/all_leaf_200wind.csv")
genes=rownames(counts)
counts_anno=cbind(genes,counts) </code></pre>
<p>I need a way to plot the counts for the bins called as significant in leafcutter. To do this I should tidy the counts data and have line and sample coulmns. Then I can create boxplots.</p>
<pre class="r"><code>counts_melt =melt(counts_anno, id.vars="genes") %>% mutate(fraction=ifelse(grepl("T", variable), "total", "nuclear")) %>% mutate(line=substr(variable,3,7)) %>% separate(col=genes, into=c("chr","bin", "gene"), sep=":")</code></pre>
<p>I can filter this for specific genes and examples. I am going to first look at the gene with the top effect size. ENSG00000066135.8</p>
<pre class="r"><code>counts_melt_ENSG00000066135.8= counts_melt %>% filter(gene=="ENSG00000066135.8") %>% arrange(bin) %>% group_by(variable) %>% mutate(sum=sum(value)) %>% ungroup(variable) %>% mutate(ratio=value/sum)</code></pre>
<p>Try to plot this.</p>
<pre class="r"><code>ggplot(counts_melt_ENSG00000066135.8, aes(x=bin, y=ratio, fill=fraction)) + geom_boxplot(width=.5) + labs(title="Used polyA sites in KDM4A by fraction", y="Read ratio")+ geom_jitter(aes(col=fraction), width=.5) </code></pre>
<p><img src="figure/dif.iso.usage.leafcutter.Rmd/unnamed-chunk-10-1.png" width="672" style="display: block; margin: auto;" /></p>
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Briana Mittleman
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<p></details></p>
<p>Look at one more gene. ENSG00000182578.9</p>
<pre class="r"><code>counts_melt_ENSG00000182578.9= counts_melt %>% filter(gene=="ENSG00000182578.9") %>% arrange(bin) %>% group_by(variable) %>% mutate(sum=sum(value)) %>% ungroup(variable) %>% mutate(ratio=value/sum)
ggplot(counts_melt_ENSG00000182578.9, aes(x=bin, y=ratio, fill=fraction)) + geom_boxplot(width=.5) + geom_jitter(aes(col=fraction),width=.5) +labs(title="Used polyA sites in CSF1R by fraction", y="Read ratio") </code></pre>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/3ab25efa4acfa013dc1e4f08224e07fcbbd2e5bb/docs/figure/dif.iso.usage.leafcutter.Rmd/unnamed-chunk-11-1.png" target="_blank">3ab25ef</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-06-06
</td>
</tr>
<tr>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/86cfd9ff2fdd20c9bcc0f2872ca016b383d81700/docs/figure/dif.iso.usage.leafcutter.Rmd/unnamed-chunk-11-1.png" target="_blank">86cfd9f</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-06-06
</td>
</tr>
</tbody>
</table>
<p></details></p>
<p>One more. ENSG00000163632.8</p>
<pre class="r"><code>counts_melt_ENSG00000163632.8= counts_melt %>% filter(gene=="ENSG00000163632.8") %>% arrange(bin) %>% group_by(variable) %>% mutate(sum=sum(value)) %>% ungroup(variable) %>% mutate(ratio=value/sum)
ggplot(counts_melt_ENSG00000163632.8, aes(x=bin, y=ratio, fill=fraction)) + geom_boxplot(width=.5) + labs(title="Used polyA sites in C3orf49 by fraction", y="Read ratio")+ geom_jitter(aes(col=fraction), width=.5) </code></pre>
<p><img src="figure/dif.iso.usage.leafcutter.Rmd/unnamed-chunk-12-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-12-1.png:</em></summary>
<table style="border-collapse:separate; border-spacing:5px;">
<thead>
<tr>
<th style="text-align:left;">
Version
</th>
<th style="text-align:left;">
Author
</th>
<th style="text-align:left;">
Date
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/1de066f7dccea7984ccb9b4602be1c0db4e4d325/docs/figure/dif.iso.usage.leafcutter.Rmd/unnamed-chunk-12-1.png" target="_blank">1de066f</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-06-06
</td>
</tr>
<tr>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/3ab25efa4acfa013dc1e4f08224e07fcbbd2e5bb/docs/figure/dif.iso.usage.leafcutter.Rmd/unnamed-chunk-12-1.png" target="_blank">3ab25ef</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-06-06
</td>
</tr>
</tbody>
</table>
<p></details></p>
<p>Top effect size in the positive direction.<br />
ENSG00000113068.5</p>
<pre class="r"><code>counts_melt_ENSG00000113068.5= counts_melt %>% filter(gene=="ENSG00000113068.5") %>% arrange(bin) %>% group_by(variable) %>% mutate(sum=sum(value)) %>% ungroup(variable) %>% mutate(ratio=value/sum)
ggplot(counts_melt_ENSG00000113068.5, aes(x=bin, y=ratio, fill=fraction)) + geom_boxplot(width=.5) + labs(title="Used polyA sites in ENSG00000113068.5 by fraction", y="Read ratio")+ geom_jitter(aes(col=fraction), width=.5) </code></pre>
<pre><code>Warning: Removed 10 rows containing non-finite values (stat_boxplot).</code></pre>
<pre><code>Warning: Removed 10 rows containing missing values (geom_point).</code></pre>
<p><img src="figure/dif.iso.usage.leafcutter.Rmd/unnamed-chunk-13-1.png" width="672" style="display: block; margin: auto;" /></p>
<p>ENSG00000138785.10</p>
<pre class="r"><code>counts_melt_ENSG00000138785.10= counts_melt %>% filter(gene=="ENSG00000138785.10") %>% arrange(bin) %>% group_by(variable) %>% mutate(sum=sum(value)) %>% ungroup(variable) %>% mutate(ratio=value/sum)
ggplot(counts_melt_ENSG00000138785.10, aes(x=bin, y=ratio, fill=fraction)) + geom_boxplot(width=.5) + labs(title="Used polyA sites in ENSG00000138785.10 by fraction", y="Read ratio")+ geom_jitter(aes(col=fraction), width=.5) </code></pre>
<p><img src="figure/dif.iso.usage.leafcutter.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>
<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/7442f068fefc39d32a79710c35ecaffc885d0563/docs/figure/dif.iso.usage.leafcutter.Rmd/unnamed-chunk-14-1.png" target="_blank">7442f06</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-06-06
</td>
</tr>
</tbody>
</table>
<p></details></p>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>R version 3.4.2 (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 edgeR_3.20.9 limma_3.34.9
[5] dplyr_0.7.5 tidyr_0.7.2 ggplot2_2.2.1 workflowr_1.0.1
[9] 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 lattice_0.20-35 evaluate_0.10.1
[13] tibble_1.4.2 gtable_0.2.0 pkgconfig_2.0.1
[16] rlang_0.2.1 yaml_2.1.19 stringr_1.3.1
[19] knitr_1.18 locfit_1.5-9.1 rprojroot_1.3-2
[22] grid_3.4.2 tidyselect_0.2.4 glue_1.2.0
[25] R6_2.2.2 purrr_0.2.5 magrittr_1.5
[28] whisker_0.3-2 backports_1.1.2 scales_0.5.0
[31] htmltools_0.3.6 assertthat_0.2.0 colorspace_1.3-2
[34] labeling_0.3 stringi_1.2.2 lazyeval_0.2.1
[37] munsell_0.4.3 R.oo_1.22.0 </code></pre>
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