Last updated: 2018-11-06

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    File Version Author Date Message
    Rmd b5f744f Briana Mittleman 2018-11-06 initiate flash2mash


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.

Steps: 1. FLASH to see tissue patterns (https://willwerscheid.github.io/MASHvFLASH/MASHvFLASHnn.html and https://willwerscheid.github.io/MASHvFLASH/MASHvFLASHnn2.html)
2. Conditional analysis with residuals to see if I can call APA qtls on the residuals from an RNA~protein analysis 3. run MASH

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.

library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ 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
── Conflicts ───────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(workflowr)
This is workflowr version 1.1.1
Run ?workflowr for help getting started

First I can use the permuted results to look at the genes that are tested in all of the phenotypes.

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)
}
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")

Now I need to look at the apa file genes.

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()

Look hoqw many genes are in all sets:

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))
[1] 904
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))
[1] 2195
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))
[1] 904
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))
[1] 904
genes_RNAProt= rna_res%>%inner_join(prot_res,by="Gene.name")
print(nrow(genes_RNAProt))
[1] 4131

Only have 904 genes that are tested in both APA and protein data.

Session information

sessionInfo()
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      



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