Last updated: 2019-07-07

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecAPAqtl.Rmd
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/PrematureTermQTL.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/propeQTLs_explained.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Deleted:    code/Upstream10Bases_general.py
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
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    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Deleted:    code/test.txt

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 bd5f228 brimittleman 2019-07-07 add nuc spec eqtl res

I want to make a qq plot for the eQTL results then seperate the values by genes with a nuclear specific apaQTL.

library(tidyverse)
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✔ readr   1.3.1       ✔ forcats 0.3.0  
── Conflicts ──────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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library(workflowr)
This is workflowr version 1.4.0
Run ?workflowr for help getting started

First i need to run the permutations on the eQTL data to get the top snp gene associations.

sbatch EandPqtl_perm.sh
cat ../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.perm.chunk* > ../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.perm.AllNomRes.txt

python changePermQTLres2geneName.py
permeqtl=read.table("../data/molQTLs/fastqtl_qqnorm_RNAseq_phase2.fixed.perm.AllNomRes.GeneName.txt", col.names = c("gene", "nvar","shape1", "shape2", "dummy", "RSID", "dist","nomPval","slope","ppval", "bpval"),stringsAsFactors = F)

permeqtl$bh=p.adjust(permeqtl$bpval, method="fdr")
qqplot(-log10(runif(nrow(permeqtl))), -log10(permeqtl$bpval),ylab="-log10 expression permuted pvalue", xlab="Uniform expectation", main="eQTL")
abline(0,1)

Seperate for genes with a nuclear specific apaQTL

nuclear specific are in ../data/QTLoverlap_nonNorm/NuclearSpecQTLinNuclearNominal_nonNorm.txt

nucSpecgene=read.table("../data/QTLoverlap_nonNorm/NuclearSpecQTLinNuclearNominal_nonNorm.txt",header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "Originalslope")) %>% separate(peakID,into=c("gene","loc", "strand", "PAS"),sep="_") %>% select(gene) %>% unique()

There are 172 genes in this set.

permeqtl_nucSpec= permeqtl  %>% filter(gene %in%nucSpecgene$gene )

permeqtl_notnucSpec= permeqtl  %>% anti_join(permeqtl_nucSpec,by = c("gene", "nvar", "shape1", "shape2", "dummy", "RSID", "dist", "nomPval", "slope", "ppval", "bpval", "bh"))
qqplot(-log10(runif(nrow(permeqtl_notnucSpec))), -log10(permeqtl_notnucSpec$bpval),ylab="-log10 expression permuted pvalue", xlab="Uniform expectation", main="eQTL")
points(sort(-log10(runif(nrow(permeqtl_nucSpec)))), sort(-log10(permeqtl_nucSpec$bpval)),col= alpha("Red"))

abline(0,1)
legend("topleft", legend=c("Nuclear specific apaQTL genes", "All other genes"),col=c("red", "black"), pch=16,bty = 'n')

wilcox.test(permeqtl_notnucSpec$ppval,permeqtl_nucSpec$ppval)

    Wilcoxon rank sum test with continuity correction

data:  permeqtl_notnucSpec$ppval and permeqtl_nucSpec$ppval
W = 114480, p-value = 0.6296
alternative hypothesis: true location shift is not equal to 0

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:
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 [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] workflowr_1.4.0 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_1.0.0       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.25.2     plyr_1.8.4       tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.12    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.10  yaml_2.2.0       haven_1.1.2     
[21] withr_2.1.2      xml2_1.2.0       httr_1.3.1       knitr_1.20      
[25] hms_0.4.2        generics_0.0.2   fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.5 glue_1.3.0       R6_2.3.0        
[33] readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2     magrittr_1.5    
[37] whisker_0.3-2    backports_1.1.2  scales_1.0.0     htmltools_0.3.6 
[41] rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2 stringi_1.2.4   
[45] lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1      crayon_1.3.4