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<h1 class="title toc-ignore">ApaQTLs with all individuals</h1>
<h4 class="author"><em>Briana Mittleman</em></h4>
<h4 class="date"><em>9/25/2018</em></h4>
</div>
<p><strong>Last updated:</strong> 2018-09-26</p>
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<p><details> <summary> <strong style="color:blue;">✔</strong> <strong>Repository version:</strong> <a href="https://github.com/brimittleman/threeprimeseq/tree/1c62f0b2dcbc05977c49cd9cf195f914a3e835fa" target="_blank">1c62f0b</a> </summary></p>
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated. <br><br> Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use <code>wflow_publish</code> or <code>wflow_git_commit</code>). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
<pre><code>
Ignored files:
Ignored: .DS_Store
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: output/.DS_Store
Untracked files:
Untracked: analysis/callMolQTLS.Rmd
Untracked: analysis/ncbiRefSeq_sm.sort.mRNA.bed
Untracked: analysis/snake.config.notes.Rmd
Untracked: analysis/verifyBAM.Rmd
Untracked: data/18486.genecov.txt
Untracked: data/APApeaksYL.total.inbrain.bed
Untracked: data/NuclearApaQTLs.txt
Untracked: data/RNAkalisto/
Untracked: data/TotalApaQTLs.txt
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Untracked: data/YL-SP-18486-T-combined-genecov.txt
Untracked: data/YL-SP-18486-T_S9_R1_001-genecov.txt
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Untracked: data/peakPerRefSeqGene/
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Unstaged changes:
Modified: analysis/28ind.peak.explore.Rmd
Modified: analysis/cleanupdtseq.internalpriming.Rmd
Modified: analysis/dif.iso.usage.leafcutter.Rmd
Modified: analysis/diff_iso_pipeline.Rmd
Modified: analysis/explore.filters.Rmd
Modified: analysis/overlap_qtls.Rmd
Modified: analysis/peakOverlap_oppstrand.Rmd
Modified: analysis/pheno.leaf.comb.Rmd
Modified: analysis/test.max2.Rmd
Modified: code/Snakefile
</code></pre>
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes. </details>
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<details> <summary> <small><strong>Expand here to see past versions:</strong></small> </summary>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/1c62f0b2dcbc05977c49cd9cf195f914a3e835fa/analysis/apaQTLsAllInd.Rmd" target="_blank">1c62f0b</a>
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Briana Mittleman
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2018-09-26
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add distribution of distance
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<a href="https://cdn.rawgit.com/brimittleman/threeprimeseq/cd3bdf842c0ab92ebb019ce969a73c61036b0fce/docs/apaQTLsAllInd.html" target="_blank">cd3bdf8</a>
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Briana Mittleman
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2018-09-26
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Build site.
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Rmd
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<a href="https://github.com/brimittleman/threeprimeseq/blob/529ace6295d7874d425999c9df32f64c8a7e143f/analysis/apaQTLsAllInd.Rmd" target="_blank">529ace6</a>
</td>
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Briana Mittleman
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2018-09-26
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add QTL res
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<a href="https://cdn.rawgit.com/brimittleman/threeprimeseq/b1bcf9916b400de7a1ae40bb2335c8ef064c9048/docs/apaQTLsAllInd.html" target="_blank">b1bcf99</a>
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Briana Mittleman
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2018-09-25
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Build site.
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<a href="https://github.com/brimittleman/threeprimeseq/blob/f4e1942a363f8a1683d49f28c4b36001bb8043e9/analysis/apaQTLsAllInd.Rmd" target="_blank">f4e1942</a>
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Briana Mittleman
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2018-09-25
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initiate all ind QTL analysis
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<p></details></p>
<hr />
<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(reshape2)</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>library(workflowr)</code></pre>
<pre><code>This is workflowr version 1.1.1
Run ?workflowr for help getting started</code></pre>
<p>I am using the code from peakOverlap_oppstrand.Rmd analysis to call QTLs on the full set of individuals. (still missing 4 due to genotype issues- Remove 18500, 19092 and 19193, 18497 - at 35).</p>
<p>Scripts:<br />
* APAqtl_nominal_oppstrand.sh</p>
<ul>
<li>APAqtl_perm_Opp.sh</li>
</ul>
<pre class="bash"><code>cat /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total* > /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_permRes.txt
cat /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear* > /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_permRes.txt
</code></pre>
<p>Write a script to ad the BH correction of the permuted QTL pvalues. I will write the plots to</p>
<p>APAqtlpermCorrectQQplot.R</p>
<pre class="r"><code>library(dplyr)
##total results
tot.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_permRes.txt",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
#BH correction
tot.perm$bh=p.adjust(tot.perm$bpval, method="fdr")
#plot qqplot
pdf("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_total_APAperm.pdf")
qqplot_total= qqplot(-log10(runif(nrow(tot.perm))), -log10(tot.perm$bpval),ylab="-log10 Total permuted pvalue", xlab="Uniform expectation", main="Total permuted pvalues for all snps")
abline(0,1)
dev.off()
#write df with BH
write.table(tot.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_permResBH.txt", col.names = T, row.names = F, quote = F)
##nuclear results
nuc.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_permRes.txt",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.perm$bh=p.adjust(nuc.perm$bpval, method="fdr")
#plot qqplot
pdf("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_nuclear_APAperm.pdf")
qqplot(-log10(runif(nrow(nuc.perm))), -log10(nuc.perm$bpval),ylab="-log10 Nuclear permuted pvalue", xlab="Uniform expectation", main="Nuclear permuted pvalues for all snps")
abline(0,1)
dev.off()
# write df with BH
write.table(nuc.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_permResBH.txt", col.names = T, row.names = F, quote = F)</code></pre>
<p>Write a script to run this:</p>
<p>run_APAqtlpermCorrectQQplot.sh</p>
<pre class="bash"><code>#!/bin/bash
#SBATCH --job-name=run_APAqtlpermCorrectQQplot
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_APAqtlpermCorrectQQplot.out
#SBATCH --error=run_APAqtlpermCorrectQQplot.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
Rscript APAqtlpermCorrectQQplot.R</code></pre>
<div id="total-results" class="section level3">
<h3>Total results</h3>
<pre class="r"><code>tot_permBH=read.table("../data/perm_QTL_opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_permResBH.txt", header=T, stringsAsFactors = F)</code></pre>
<p>Check to quality of the tests:</p>
<pre class="r"><code>plot(tot_permBH$ppval, tot_permBH$bpval, xlab="Direct method", ylab="Beta approximation", main="Total Check plot")
abline(0, 1, col="red")</code></pre>
<p><img src="figure/apaQTLsAllInd.Rmd/unnamed-chunk-6-1.png" width="672" style="display: block; margin: auto;" /></p>
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<p></details></p>
<pre class="r"><code>plot(-log10(tot_permBH$bh), main="Total BH corrected pval")
abline(h=1,col="Red")</code></pre>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/cd3bdf842c0ab92ebb019ce969a73c61036b0fce/docs/figure/apaQTLsAllInd.Rmd/unnamed-chunk-7-1.png" target="_blank">cd3bdf8</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-09-26
</td>
</tr>
</tbody>
</table>
<p></details></p>
<p>I am going to look how many variants pass the 10% FDR.</p>
<pre class="r"><code>tot_qtl_10= tot_permBH %>% filter(-log10(bh) > 1)
nrow(tot_qtl_10)</code></pre>
<pre><code>[1] 1468</code></pre>
<p>This is not accounting for the same peak in multiple genes. I want to look at the number of unique snps that are significant.</p>
<pre class="r"><code>tot_qtl_10uniq= tot_permBH %>% filter(-log10(bh) > 1) %>% summarise(n_distinct(sid))
tot_qtl_10uniq</code></pre>
<pre><code> n_distinct(sid)
1 568</code></pre>
</div>
<div id="nuclear-results" class="section level3">
<h3>Nuclear results</h3>
<pre class="r"><code>nuc_permBH=read.table("../data/perm_QTL_opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_permResBH.txt", header=T, stringsAsFactors = F)</code></pre>
<p>Check to quality of the tests:</p>
<pre class="r"><code>plot(nuc_permBH$ppval, nuc_permBH$bpval, xlab="Direct method", ylab="Beta approximation", main="Nuclear Check plot")
abline(0, 1, col="red")</code></pre>
<p><img src="figure/apaQTLsAllInd.Rmd/unnamed-chunk-11-1.png" width="672" style="display: block; margin: auto;" /></p>
<details> <summary><em>Expand here to see past versions of unnamed-chunk-11-1.png:</em></summary>
<table style="border-collapse:separate; border-spacing:5px;">
<thead>
<tr>
<th style="text-align:left;">
Version
</th>
<th style="text-align:left;">
Author
</th>
<th style="text-align:left;">
Date
</th>
</tr>
</thead>
<tbody>
<tr>
<td style="text-align:left;">
<a href="https://github.com/brimittleman/threeprimeseq/blob/cd3bdf842c0ab92ebb019ce969a73c61036b0fce/docs/figure/apaQTLsAllInd.Rmd/unnamed-chunk-11-1.png" target="_blank">cd3bdf8</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-09-26
</td>
</tr>
</tbody>
</table>
<p></details></p>
<pre class="r"><code>plot(-log10(nuc_permBH$bh), main="Nuclear BH corrected pval")
abline(h=1,col="Red")</code></pre>
<p><img src="figure/apaQTLsAllInd.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/cd3bdf842c0ab92ebb019ce969a73c61036b0fce/docs/figure/apaQTLsAllInd.Rmd/unnamed-chunk-12-1.png" target="_blank">cd3bdf8</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-09-26
</td>
</tr>
</tbody>
</table>
<p></details></p>
<p>I am going to look how many variants pass the 10% FDR.</p>
<pre class="r"><code>nuc_qtl_10= nuc_permBH %>% filter(-log10(bh) > 1)
nrow(nuc_qtl_10)</code></pre>
<pre><code>[1] 7025</code></pre>
<p>This is not accounting for the same peak in multiple genes. I want to look at the number of unique snps that are significant.</p>
<pre class="r"><code>nuc_qtl_10uniq= nuc_permBH %>% filter(-log10(bh) > 1) %>% summarise(n_distinct(sid))
nuc_qtl_10uniq</code></pre>
<pre><code> n_distinct(sid)
1 2736</code></pre>
</div>
<div id="compare-number-of-sig-qtls-by-fdr-cuttoff" class="section level3">
<h3>Compare number of sig QTLs by FDR cuttoff</h3>
<pre class="r"><code>nQTL_tot=c()
FDR=seq(.05, .5, .01)
for (i in FDR){
x=tot_permBH %>% filter(bh < i ) %>% nrow()
nQTL_tot=c(nQTL_tot, x)
}
FDR=seq(.05, .5, .01)
nQTL_nuc=c()
for (i in FDR){
x=nuc_permBH %>% filter(bh < i ) %>% nrow()
nQTL_nuc=c(nQTL_nuc, x)
}
nQTL=as.data.frame(cbind(FDR, Total=nQTL_tot, Nuclear=nQTL_nuc))
nQTL_long=melt(nQTL, id.vars = "FDR")
ggplot(nQTL_long, aes(x=FDR, y=value, by=variable, col=variable)) + geom_line(size=1.5) + labs(y="Number of Significant QTLs", title="APAqtls detected by FDR cuttoff", color="Fraction")</code></pre>
<p><img src="figure/apaQTLsAllInd.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>
<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/cd3bdf842c0ab92ebb019ce969a73c61036b0fce/docs/figure/apaQTLsAllInd.Rmd/unnamed-chunk-15-1.png" target="_blank">cd3bdf8</a>
</td>
<td style="text-align:left;">
Briana Mittleman
</td>
<td style="text-align:left;">
2018-09-26
</td>
</tr>
</tbody>
</table>
<p></details></p>
</div>
<div id="explore-qtls" class="section level3">
<h3>Explore QTLs</h3>
<p>Look at distribution of SNP to peak in each fraction:</p>
<pre class="r"><code>ggplot(nuc_qtl_10, aes(x=log10(abs(dist) + 1)) )+ geom_histogram(binwidth=.15, alpha=.5 ) + geom_histogram(data=tot_qtl_10, aes(x=log10(abs(dist) + 1)),fill="Red", alpha=.5,binwidth=.15) + annotate("text", x=1, y=950, col="Red", label="Total") + annotate("text", x=1, y=900, col="Black", label="Nuclear") + geom_rect(linetype=1, xmin=.5, xmax=1.5, ymin=850, ymax=1000, color="Black", alpha=0)</code></pre>
<p><img src="figure/apaQTLsAllInd.Rmd/unnamed-chunk-16-1.png" width="672" style="display: block; margin: auto;" /></p>
<pre class="r"><code>ggplot(nuc_qtl_10, aes(x=log10(abs(dist) + 1)) )+ geom_density( alpha=.25 ,fill="Black") + geom_density(data=tot_qtl_10, aes(x=log10(abs(dist) + 1)),fill="Red", alpha=.25) + annotate("text", x=1, y=.77, col="Red", label="Total") + annotate("text", x=1, y=.72, col="Black", label="Nuclear") + geom_rect(linetype=1, xmin=.5, xmax=1.5, ymin=.69, ymax=.8, color="Black", alpha=0)</code></pre>
<p><img src="figure/apaQTLsAllInd.Rmd/unnamed-chunk-17-1.png" width="672" style="display: block; margin: auto;" /></p>
</div>
<div id="session-information" class="section level2">
<h2>Session information</h2>
<pre class="r"><code>sessionInfo()</code></pre>
<pre><code>R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] bindrcpp_0.2.2 workflowr_1.1.1 reshape2_1.4.3 forcats_0.3.0
[5] stringr_1.3.1 dplyr_0.7.6 purrr_0.2.5 readr_1.1.1
[9] tidyr_0.8.1 tibble_1.4.2 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_2.2.0
[7] rlang_0.2.2 R.oo_1.22.0 pillar_1.3.0
[10] glue_1.3.0 withr_2.1.2 R.utils_2.7.0
[13] modelr_0.1.2 readxl_1.1.0 bindr_0.1.1
[16] plyr_1.8.4 munsell_0.5.0 gtable_0.2.0
[19] cellranger_1.1.0 rvest_0.3.2 R.methodsS3_1.7.1
[22] evaluate_0.11 labeling_0.3 knitr_1.20
[25] broom_0.5.0 Rcpp_0.12.18 scales_1.0.0
[28] backports_1.1.2 jsonlite_1.5 hms_0.4.2
[31] digest_0.6.16 stringi_1.2.4 grid_3.5.1
[34] rprojroot_1.3-2 cli_1.0.0 tools_3.5.1
[37] magrittr_1.5 lazyeval_0.2.1 crayon_1.3.4
[40] whisker_0.3-2 pkgconfig_2.0.2 xml2_1.2.0
[43] lubridate_1.7.4 assertthat_0.2.0 rmarkdown_1.10
[46] httr_1.3.1 rstudioapi_0.7 R6_2.2.2
[49] nlme_3.1-137 git2r_0.23.0 compiler_3.5.1 </code></pre>
</div>
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