Last updated: 2020-02-20

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Unstaged changes:
    Modified:   analysis/ExploreNpas.Rmd
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/PASdescriptiveplots.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/TSS.Rmd
    Modified:   analysis/decayAndStability.Rmd
    Modified:   analysis/miRNAdisrupt.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/nucSpecinEQTLs.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/pQTLexampleplot.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/DistPAS2Sig.py
    Modified:   code/Script4NuclearQTLexamples.sh
    Modified:   code/Script4TotalQTLexamples.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/environment.yaml
    Modified:   code/run_qtlFacetBoxplots.sh
    Deleted:    code/test.txt
    Deleted:    reads_graphs.Rmd

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.

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Rmd d2056c1 brimittleman 2020-02-20 add lcls, add coloc package, add 5’ss by decile

Attempt a colocalziation analysis for the eQTLs and the snps in the nuclear QTL data.

mkdir ../data/coloc
wget http://eqtl.uchicago.edu/jointLCL/output_RNAseqGeuvadis_PC14.txt
#install.packages("coloc")
library(tidyverse)
── Attaching packages ────────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ───────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library("coloc")

First change the names of the rsid and gene.

geneNames=read.table("../../genome_anotation_data/ensemble_to_genename.txt", sep="\t", col.names = c('gene_id', 'GeneName', 'source' ),stringsAsFactors = F, header = T)
RSID=read.table("/project2/gilad/briana/li_genotypes/RSID2snploc.txt",header = T, stringsAsFactors = F)
#ed 's/^chr//' output_RNAseqGeuvadis_PC14.txt  > output_RNAseqGeuvadis_PC14.noChr.txt 

eQTLres=read.table("../data/coloc/output_RNAseqGeuvadis_PC14.noChr.txt",header = T, stringsAsFactors = F)  %>% separate(snps,into=c("chr", "pos"), sep="\\.")%>% separate(gene,into=c("gene_id", "extra"), sep="\\.")  %>% mutate(snp=paste(chr,pos, sep=":")) %>% inner_join(RSID,by="snp") %>% inner_join(geneNames, by="gene_id") %>% select(RSID, GeneName,pvalue, beta)

I will be able to seperate these for the coloc analysis. I need the PAS gene pairs.

PASgene=read.table("../data/eQTL_LZ/PasGENEsnpstoUse.txt",stringsAsFactors = F, col.names = c("GeneName", "PAS", "RSID") )

Test if these are in the file above:

eQTLres_genesnp=eQTLres %>% mutate(test=paste(GeneName, RSID, sep="_")) %>% select(test)
PASgene_test= PASgene %>%  mutate(test=paste(GeneName, RSID, sep="_")) %>% select(test)


PASgene_indata= PASgene_test %>% inner_join(eQTLres_genesnp, by="test")

PASgene_indata= PASgene_test %>% anti_join(eQTLres_genesnp, by="test")

181 of the snp gene pairs are in this data.


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] coloc_3.2-1     forcats_0.3.0   stringr_1.3.1   dplyr_0.8.0.1  
 [5] purrr_0.3.2     readr_1.3.1     tidyr_0.8.3     tibble_2.1.1   
 [9] ggplot2_3.1.1   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.2          lubridate_1.7.4     mvtnorm_1.0-8      
 [4] lattice_0.20-38     assertthat_0.2.0    rprojroot_1.3-2    
 [7] digest_0.6.18       R6_2.3.0            cellranger_1.1.0   
[10] plyr_1.8.4          backports_1.1.2     stats4_3.5.1       
[13] pcaPP_1.9-73        evaluate_0.12       httr_1.3.1         
[16] pillar_1.3.1        zlibbioc_1.28.0     rlang_0.4.0        
[19] lazyeval_0.2.1      readxl_1.1.0        rstudioapi_0.10    
[22] whisker_0.3-2       Matrix_1.2-15       rmarkdown_1.10     
[25] splines_3.5.1       munsell_0.5.0       broom_0.5.1        
[28] compiler_3.5.1      httpuv_1.4.5        modelr_0.1.2       
[31] BiocGenerics_0.28.0 pkgconfig_2.0.2     htmltools_0.3.6    
[34] tidyselect_0.2.5    workflowr_1.6.0     reshape_0.8.8      
[37] BMA_3.18.11         crayon_1.3.4        rrcov_1.4-7        
[40] withr_2.1.2         later_0.7.5         leaps_3.1          
[43] grid_3.5.1          nlme_3.1-137        jsonlite_1.6       
[46] gtable_0.2.0        git2r_0.26.1        magrittr_1.5       
[49] scales_1.0.0        cli_1.1.0           stringi_1.2.4      
[52] fs_1.3.1            promises_1.0.1      robustbase_0.93-3  
[55] xml2_1.2.0          snpStats_1.32.0     generics_0.0.2     
[58] tools_3.5.1         glue_1.3.0          DEoptimR_1.0-8     
[61] hms_0.4.2           parallel_3.5.1      survival_2.43-1    
[64] yaml_2.2.0          inline_0.3.15       colorspace_1.3-2   
[67] cluster_2.0.7-1     rvest_0.3.2         knitr_1.20         
[70] haven_1.1.2