Last updated: 2019-06-20

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecAPAqtl.Rmd
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    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
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library(tidyverse)
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library(cowplot)

Attaching package: 'cowplot'
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    ggsave
library(ggpubr)
Loading required package: magrittr

Attaching package: 'magrittr'
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    set_names
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    extract

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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 )
ggplot(NuclearSpecQTL,aes(x=Loc, fill=Shared)) + geom_bar()

NuclearSpecQTL__group= NuclearSpecQTL %>% group_by(Loc, Shared) %>% summarise(nShared=n()) %>% ungroup() %>% group_by(Loc) %>% mutate(nLoc=sum(nShared)) %>% ungroup() %>% mutate(prop=nShared/nLoc)


ggplot(NuclearSpecQTL__group, aes(x=Loc, y=prop, fill=Shared)) + geom_bar(stat="identity") + labs(title="Proportion of apaQTL by \nlocation that are nuclear specific")

NuclearSpecQTL__group_small=NuclearSpecQTL__group %>% filter( Loc=="intron" |Loc=="utr3")

ggplot(NuclearSpecQTL__group_small, aes(x=Loc, y=prop, fill=Shared)) + geom_bar(stat="identity") + labs(title="Proportion of apaQTL by \nlocation that are nuclear specific") 

NuclearSpecQTL__group_small
# A tibble: 4 x 5
  Loc    Shared nShared  nLoc  prop
  <chr>  <fct>    <int> <int> <dbl>
1 intron No         183   297 0.616
2 intron Yes        114   297 0.384
3 utr3   No          87   355 0.245
4 utr3   Yes        268   355 0.755
prop.test(x=c(183,87),n=c(297,355))

    2-sample test for equality of proportions with continuity
    correction

data:  c(183, 87) out of c(297, 355)
X-squared = 90.261, df = 1, p-value < 2.2e-16
alternative hypothesis: two.sided
95 percent confidence interval:
 0.2968583 0.4453241
sample estimates:
   prop 1    prop 2 
0.6161616 0.2450704

I want to know if the shared or specific are more likely to decrease/increase

NuclearSpecQTL=NuclearSpecQTL %>% mutate(Dir=ifelse(slope>1, "Increase", "Decrease"))

NuclearSpecQTL_shareInc=NuclearSpecQTL %>% filter(Loc=="intron",Dir=="Increase", Shared=="Yes") %>% nrow()
AllShared=NuclearSpecQTL %>%  filter(Loc=="intron", Shared=="Yes") %>% nrow()
AllInc=NuclearSpecQTL %>%  filter(Loc=="intron", Dir=="Increase") %>% nrow()
AllDec=NuclearSpecQTL %>%  filter(Loc=="intron", Dir=="Decrease") %>% nrow()
AllSpec=NuclearSpecQTL %>%  filter(Loc=="intron", Shared=="No") %>% nrow()
NuclearSpecQTL_SpecInc=NuclearSpecQTL %>% filter(Loc=="intron",Dir=="Increase", Shared=="No") %>% nrow()
NuclearSpecQTL_shareDec=NuclearSpecQTL %>% filter(Loc=="intron",Dir=="Decrease", Shared=="Yes") %>% nrow()
NuclearSpecQTL_SpecDec=NuclearSpecQTL %>% filter(Loc=="intron",Dir=="Decrease", Shared=="No") %>% nrow()

#in increased
NuclearSpecQTL_SpecInc/AllInc
[1] 0.5701754
#in dec
NuclearSpecQTL_SpecDec/AllDec
[1] 0.6448087
prop.test(x=c(NuclearSpecQTL_SpecInc,NuclearSpecQTL_SpecDec), n=c(AllInc,AllDec))

    2-sample test for equality of proportions with continuity
    correction

data:  c(NuclearSpecQTL_SpecInc, NuclearSpecQTL_SpecDec) out of c(AllInc, AllDec)
X-squared = 1.3538, df = 1, p-value = 0.2446
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.19605819  0.04679158
sample estimates:
   prop 1    prop 2 
0.5701754 0.6448087 
ggplot(NuclearSpecQTL, aes(x=Dir, fill=Shared))+ geom_bar(stat="count") + facet_grid(~Loc) +  theme(axis.text.x=element_text(angle=90, hjust=1))


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] ggpubr_0.2      magrittr_1.5    cowplot_0.9.4   forcats_0.3.0  
 [5] stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1    
 [9] tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.1   tidyverse_1.2.1
[13] workflowr_1.3.0

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 reshape2_1.4.3   haven_1.1.2      lattice_0.20-38 
 [5] colorspace_1.3-2 generics_0.0.2   htmltools_0.3.6  yaml_2.2.0      
 [9] utf8_1.1.4       rlang_0.3.1      pillar_1.3.1     glue_1.3.0      
[13] withr_2.1.2      modelr_0.1.2     readxl_1.1.0     plyr_1.8.4      
[17] munsell_0.5.0    gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2     
[21] evaluate_0.12    labeling_0.3     knitr_1.20       fansi_0.4.0     
[25] broom_0.5.1      Rcpp_1.0.0       scales_1.0.0     backports_1.1.2 
[29] jsonlite_1.6     fs_1.2.6         hms_0.4.2        digest_0.6.18   
[33] stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2  cli_1.0.1       
[37] tools_3.5.1      lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2   
[41] pkgconfig_2.0.2  xml2_1.2.0       lubridate_1.7.4  assertthat_0.2.0
[45] rmarkdown_1.10   httr_1.3.1       rstudioapi_0.10  R6_2.3.0        
[49] nlme_3.1-137     git2r_0.25.2     compiler_3.5.1