Last updated: 2019-07-26

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Knit directory: apaQTL/analysis/

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    Modified:   analysis/DiffIsoAnalysis.Rmd
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File Version Author Date Message
Rmd cee6ce0 brimittleman 2019-07-26 get pvalues form <-16 tests
html f7e0fe5 brimittleman 2019-06-20 Build site.
Rmd b7c9381 brimittleman 2019-06-20 test inc/dec
html cd60f50 brimittleman 2019-06-20 Build site.
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 1.4.0
Run ?workflowr for help getting started
library(tidyverse)
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library(cowplot)

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    ggsave
library(ggpubr)
Loading required package: magrittr

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    set_names
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    extract

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    get_legend

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" ))
allShare=NuclearSpecQTL_gene %>% filter(Shared=="Yes")
nEandShare
[1] 113
nEandNotShare=nrow(NuclearSpecQTL_gene %>% filter(Shared=="No", uneplained=="Yes" |explained=="Yes"))
nEandNotShare
[1] 59
allNotShare=NuclearSpecQTL_gene %>% filter(Shared=="No")

prop.test(x=c(nEandShare,nEandNotShare),n=c(nrow(allShare),nrow(allNotShare)))

    2-sample test for equality of proportions with continuity
    correction

data:  c(nEandShare, nEandNotShare) out of c(nrow(allShare), nrow(allNotShare))
X-squared = 5.3642, df = 1, p-value = 0.02055
alternative hypothesis: two.sided
95 percent confidence interval:
 0.01213795 0.13376403
sample estimates:
   prop 1    prop 2 
0.2539326 0.1809816

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()

Version Author Date
f7e0fe5 brimittleman 2019-06-20 
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")

Version Author Date
f7e0fe5 brimittleman 2019-06-20 
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", y="Proportion of QTLs") + scale_fill_discrete(labels = c("Specific","Shared"))  + scale_fill_manual(values=c("orange", "blue"))
Scale for 'fill' is already present. Adding another scale for 'fill',
which will replace the existing scale.

Version Author Date
f7e0fe5 brimittleman 2019-06-20 
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 
prop.test(x=c(183,87),n=c(297,355))$p.value
[1] 2.087491e-21

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))

Version Author Date
f7e0fe5 brimittleman 2019-06-20 

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_1.4.0

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_0.4.0      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] highr_0.7        broom_0.5.1      Rcpp_1.0.0       scales_1.0.0    
[29] backports_1.1.2  jsonlite_1.6     fs_1.3.1         hms_0.4.2       
[33] digest_0.6.18    stringi_1.2.4    grid_3.5.1       rprojroot_1.3-2 
[37] cli_1.1.0        tools_3.5.1      lazyeval_0.2.1   crayon_1.3.4    
[41] whisker_0.3-2    pkgconfig_2.0.2  xml2_1.2.0       lubridate_1.7.4 
[45] assertthat_0.2.0 rmarkdown_1.10   httr_1.3.1       rstudioapi_0.10 
[49] R6_2.3.0         nlme_3.1-137     git2r_0.25.2     compiler_3.5.1