Last updated: 2019-06-20

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecAPAqtl.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
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    Modified:   code/apaQTLCorrectPvalMakeQQ.R
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    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Deleted:    code/test.txt

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Rmd 6df08b6 brimittleman 2019-06-20 change analysis to include not tested in total as nuc spec

In my previous analysis found here I took nuclear specific apa QTLs as those tested in total that are not nominally significant in total. In this analysis I will include the nuclear apaQTLs in PAS not tested in total as nuclear specific. These may be important for explaining eQTLs or pQTLs.

library(workflowr)
This is workflowr version 1.3.0
Run ?workflowr for help getting started
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(cowplot)

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave

I will give all of the QTLs an id.

nucQTls=read.table("../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt",header = T, stringsAsFactors = F) %>% mutate(ID=paste(Gene,Peak, sid, sep=":"))
sharedQTLs=read.table("../data/apaQTLs/SharedAPAQTLs.txt", header = T, stringsAsFactors = F) %>% mutate(ID=paste(gene,peakNum, snp, sep=":"))

sharedQTL_ID=as.vector(sharedQTLs$ID)

Nuclear Specific:

NuclearSpecQTL= nucQTls %>% mutate(Shared=ifelse(ID %in% sharedQTL_ID, "Yes", "No"))
NuclearSpecQTL$Shared=as.factor(NuclearSpecQTL$Shared)

I need to input the explained eGenes, unexplained eGenes, and pGenes. For this I will make sure none of the pgenes are eGenes.

explained=read.table("../data/Li_eQTLs/explainedEgenes.txt", header = F, stringsAsFactors = F, col.names = c("gene"))
unexplained=read.table("../data/Li_eQTLs/UnexplainedEgenes.txt", header = F, stringsAsFactors = F, col.names = c("gene"))
protein=read.table("../data/Battle_pQTL/psQTLGeneNames.txt",header = F, stringsAsFactors = F,col.names = c("gene"))
'%!in%' <- function(x,y)!('%in%'(x,y))


protein_only=protein %>% filter(gene %!in% explained$gene & gene %!in% unexplained$gene)

write.table(protein_only, "../data/Battle_pQTL/pQTLGeneNamesONLYP.txt", col.names = F, row.names = F,quote = F, sep="\t")

Are nuc specific less likely to be in p genes?

NuclearSpecQTL_gene=NuclearSpecQTL %>% mutate(pGene=ifelse(Gene %in% protein_only$gene, "Yes", "No"), uneplained=ifelse(Gene %in% unexplained$gene, "Yes", "No"), explained=ifelse(Gene %in% explained$gene, "Yes","No"))
nPandShare=nrow(NuclearSpecQTL_gene %>% filter(Shared=="Yes", pGene=="Yes"))/nrow(NuclearSpecQTL_gene)
nPandShare
[1] 0.01037613
nPandNotShare=nrow(NuclearSpecQTL_gene %>% filter(Shared=="No", pGene=="Yes"))/nrow(NuclearSpecQTL_gene)
nPandNotShare
[1] 0.002594034

Only looking at 8 and 2. This isnt very good. Cant make claim.

nEandShare=nrow(NuclearSpecQTL_gene %>% filter(Shared=="Yes", uneplained=="Yes" |explained=="Yes" ))

nEandShare
[1] 113
nEandNotShare=nrow(NuclearSpecQTL_gene %>% filter(Shared=="No", uneplained=="Yes" |explained=="Yes"))
nEandNotShare
[1] 59
prop.test(x=c(nEandShare,nEandNotShare),n=c(nrow(NuclearSpecQTL_gene),nrow(NuclearSpecQTL_gene)))

    2-sample test for equality of proportions with continuity
    correction

data:  c(nEandShare, nEandNotShare) out of c(nrow(NuclearSpecQTL_gene), nrow(NuclearSpecQTL_gene))
X-squared = 18.382, df = 1, p-value = 1.808e-05
alternative hypothesis: two.sided
95 percent confidence interval:
 0.03751188 0.10256594
sample estimates:
    prop 1     prop 2 
0.14656291 0.07652399

I want to not count genes with multiple qtl

nGenes=NuclearSpecQTL_gene %>% group_by(Gene) %>% summarise(n=n()) %>% nrow()
nGenes
[1] 609
Egeneandshared=NuclearSpecQTL_gene %>% filter(Shared=="Yes", uneplained=="Yes" |explained=="Yes" ) %>% group_by(Gene) %>% summarise(n=n()) %>% nrow()
Egeneandshared
[1] 89
EgeneandNotshared=NuclearSpecQTL_gene %>% filter(Shared=="No", uneplained=="Yes" |explained=="Yes" ) %>% group_by(Gene) %>% summarise(n=n()) %>% nrow()
EgeneandNotshared
[1] 53
prop.test(x=c(Egeneandshared,EgeneandNotshared),n=c(nGenes,nGenes))

    2-sample test for equality of proportions with continuity
    correction

data:  c(Egeneandshared, EgeneandNotshared) out of c(nGenes, nGenes)
X-squared = 9.7652, df = 1, p-value = 0.001778
alternative hypothesis: two.sided
95 percent confidence interval:
 0.02157843 0.09664817
sample estimates:
    prop 1     prop 2 
0.14614122 0.08702791

This is significant. This means the extra PAS are most likely driving the egene overlap.

Write these out for other anaylsis.

NuclearSpecQTL_shared= NuclearSpecQTL %>% filter(Shared=="Yes") %>% select(Gene, sid)
write.table(NuclearSpecQTL_shared,file="../data/NucSpeceQTLeffect/SharedApaQTL_nottestinc.txt", col.names = F, row.names = F, sep="\t", quote = F )
NuclearSpecQTL_specific=NuclearSpecQTL %>% filter(Shared=="No")%>% select(Gene, sid)
write.table(NuclearSpecQTL_specific,file="../data/NucSpeceQTLeffect/NucSpecApaQTL_nottestinc.txt", col.names = F, row.names = F, sep="\t", quote = F )

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] cowplot_0.9.4   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 workflowr_1.3.0

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