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<h1 class="title toc-ignore">FLASH2MASH</h1>
<h4 class="author"><em>Briana Mittleman</em></h4>
<h4 class="date"><em>11/6/2018</em></h4>
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<p><strong>Last updated:</strong> 2018-11-06</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/b5f744f599ad5ec725eb3e31b86e804979f568b2" target="_blank">b5f744f</a> </summary></p>
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<a href="https://github.com/brimittleman/threeprimeseq/blob/b5f744f599ad5ec725eb3e31b86e804979f568b2/analysis/flash2mash.Rmd" target="_blank">b5f744f</a>
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Briana Mittleman
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2018-11-06
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initiate flash2mash
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<p></details></p>
<hr />
<p>I will use this analysis to implement the flash and mash packages developed by the stephens lab to better understand molecular QTL sharing and to see if adding APA to a model can help with power in protein QTLs.</p>
<p>Steps: 1. FLASH to see tissue patterns (<a href="https://willwerscheid.github.io/MASHvFLASH/MASHvFLASHnn.html" class="uri">https://willwerscheid.github.io/MASHvFLASH/MASHvFLASHnn.html</a> and <a href="https://willwerscheid.github.io/MASHvFLASH/MASHvFLASHnn2.html" class="uri">https://willwerscheid.github.io/MASHvFLASH/MASHvFLASHnn2.html</a>)<br />
2. Conditional analysis with residuals to see if I can call APA qtls on the residuals from an RNA~protein analysis 3. run MASH</p>
<p>Data stucture: I need to have a matrix with all of my QTL results. I want to get a snp-gene by phenotype matrix with the effect sizes and standard errors. First I will do this with the genes we have all data for (unless it is too small). To deal with the APA isoform problem I will use the peak with the most significant peak-snp pair. This should be ok because given the peaks are ratios they are all correlated with eachother.</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>
<p>First I can use the permuted results to look at the genes that are tested in all of the phenotypes.</p>
<pre class="r"><code>read_permfile=function(file, mol){
perm_names=c("pid" ,"nvar","shape1" ,"shape2", "dummy","sid" ,"dist","npval", "slope" , "ppval" ,"bpval")
geneNames=read.table("../data/ensemble_to_genename.txt", sep="\t", header=T,stringsAsFactors = F)
res=read.table(file, col.names = perm_names, stringsAsFactors = F)
if (mol == "protein"){
res_f= res %>% rename("Gene.stable.ID"=pid)
res_final= res_f %>% inner_join(geneNames, by="Gene.stable.ID") %>% select(c("Gene.name"))
}
else{
res_final =res %>% separate(pid, into=c("Gene.stable.ID", "ver"), sep ="[.]") %>% inner_join(geneNames, by="Gene.stable.ID") %>% select(c("Gene.name"))
}
return(res_final)
}</code></pre>
<pre class="r"><code>prot_res=read_permfile("../data/other_qtls/fastqtl_qqnorm_prot.fixed.perm.out", "protein")
rna_res=read_permfile("../data/other_qtls/fastqtl_qqnorm_RNAseq_phase2.fixed.perm.out", "RNA")
rnaG_res=read_permfile("../data/other_qtls/fastqtl_qqnorm_RNAseqGeuvadis.fixed.perm.out", "RNAG")
su30_res=read_permfile("../data/other_qtls/fastqtl_qqnorm_4su30.fixed.perm.out", "su30")
su60_res=read_permfile("../data/other_qtls/fastqtl_qqnorm_4su60.fixed.perm.out", "su60")
ribo_res=read_permfile("../data/other_qtls/fastqtl_qqnorm_ribo_phase2.fixed.perm.out", "ribo")</code></pre>
<p>Now I need to look at the apa file genes.</p>
<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) %>% separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% rename("Gene.name"=gene) %>% select(Gene.name)%>% distinct()
totalAPA=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_transcript_permResBH.txt", stringsAsFactors = F, header=T) %>% separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% rename("Gene.name"=gene) %>% select(Gene.name) %>% distinct()</code></pre>
<p>Look hoqw many genes are in all sets:</p>
<pre class="r"><code>allgenes= NuclearAPA %>% inner_join(totalAPA,by="Gene.name") %>% inner_join(totalAPA,by="Gene.name") %>% inner_join(su30_res,by="Gene.name") %>% inner_join(su60_res,by="Gene.name") %>% inner_join(rna_res,by="Gene.name") %>% inner_join(rnaG_res,by="Gene.name")%>% inner_join(ribo_res,by="Gene.name")%>% inner_join(prot_res,by="Gene.name")
print(nrow(allgenes))</code></pre>
<pre><code>[1] 904</code></pre>
<pre class="r"><code>allgenes_minusprot= NuclearAPA %>% inner_join(totalAPA,by="Gene.name") %>% inner_join(totalAPA,by="Gene.name") %>% inner_join(su30_res,by="Gene.name") %>% inner_join(su60_res,by="Gene.name") %>% inner_join(rna_res,by="Gene.name") %>% inner_join(rnaG_res,by="Gene.name")%>% inner_join(ribo_res,by="Gene.name")
print(nrow(allgenes_minusprot))</code></pre>
<pre><code>[1] 2195</code></pre>
<pre class="r"><code>allgenes_minusribo= NuclearAPA %>% inner_join(totalAPA,by="Gene.name") %>% inner_join(totalAPA,by="Gene.name") %>% inner_join(su30_res,by="Gene.name") %>% inner_join(su60_res,by="Gene.name") %>% inner_join(rna_res,by="Gene.name") %>% inner_join(rnaG_res,by="Gene.name")%>% inner_join(prot_res,by="Gene.name")
print(nrow(allgenes_minusribo))</code></pre>
<pre><code>[1] 904</code></pre>
<pre class="r"><code>genes_ApaRnaProt= NuclearAPA %>% inner_join(totalAPA,by="Gene.name") %>%inner_join(rna_res,by="Gene.name") %>%inner_join(prot_res,by="Gene.name")
print(nrow(genes_ApaRnaProt))</code></pre>
<pre><code>[1] 904</code></pre>
<pre class="r"><code>genes_RNAProt= rna_res%>%inner_join(prot_res,by="Gene.name")
print(nrow(genes_RNAProt))</code></pre>
<pre><code>[1] 4131</code></pre>
<p>Only have 904 genes that are tested in both APA and protein data.</p>
<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] workflowr_1.1.1 forcats_0.3.0 stringr_1.3.1 dplyr_0.7.6
[5] purrr_0.2.5 readr_1.1.1 tidyr_0.8.1 tibble_1.4.2
[9] ggplot2_3.0.0 tidyverse_1.2.1
loaded via a namespace (and not attached):
[1] Rcpp_0.12.19 cellranger_1.1.0 plyr_1.8.4
[4] compiler_3.5.1 pillar_1.3.0 git2r_0.23.0
[7] bindr_0.1.1 R.methodsS3_1.7.1 R.utils_2.7.0
[10] tools_3.5.1 digest_0.6.17 lubridate_1.7.4
[13] jsonlite_1.5 evaluate_0.11 nlme_3.1-137
[16] gtable_0.2.0 lattice_0.20-35 pkgconfig_2.0.2
[19] rlang_0.2.2 cli_1.0.1 rstudioapi_0.8
[22] yaml_2.2.0 haven_1.1.2 bindrcpp_0.2.2
[25] withr_2.1.2 xml2_1.2.0 httr_1.3.1
[28] knitr_1.20 hms_0.4.2 rprojroot_1.3-2
[31] grid_3.5.1 tidyselect_0.2.4 glue_1.3.0
[34] R6_2.3.0 readxl_1.1.0 rmarkdown_1.10
[37] modelr_0.1.2 magrittr_1.5 whisker_0.3-2
[40] backports_1.1.2 scales_1.0.0 htmltools_0.3.6
[43] rvest_0.3.2 assertthat_0.2.0 colorspace_1.3-2
[46] stringi_1.2.4 lazyeval_0.2.1 munsell_0.5.0
[49] broom_0.5.0 crayon_1.3.4 R.oo_1.22.0 </code></pre>
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