Last updated: 2019-06-16

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Knit directory: apaQTL/analysis/

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File Version Author Date Message
Rmd 4c79f1c brimittleman 2019-06-16 write out nuc spec qtls
html 3fbf3f3 brimittleman 2019-06-14 Build site.
Rmd dc8d012 brimittleman 2019-06-14 add nuclear specific analysis

I want to look at nuclear specific apaQTLs. I expect these to be eQTls.

To look at nuclear specific apaQTLs I will test if nuclear QTLs are nominally significant in the total fraction. I created this file in this analysis where I tested qtl overlap.

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

I test 589 of the apaQTls in the total fraction.

nucAPAinTot=read.table("../data/QTLoverlap/NuclearQTLinTotalNominal.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope")) %>% separate(peakID,into=c("chr", "start", "end", "geneID"),sep=":" ) %>% separate(geneID, into=c("gene", "loc", "strand", "peakNum"), sep="_")
nucAPAinTot_NOTSIG=nucAPAinTot %>% filter(pval>.05)
nucAPAinTot_SIG=nucAPAinTot %>% filter(pval<.05)
hist(nucAPAinTot$pval, breaks=100)

ggplot(nucAPAinTot, aes(x=pval)) + geom_density() + geom_vline(xintercept = .05)

I can test if these are more likely to be eGenes. I can get the eGenes by comparing the

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"))

allEgenes=as.data.frame(rbind(explained,unexplained))
nucAPAinTot_NOTSIG_egene=nucAPAinTot_NOTSIG %>% semi_join(allEgenes, by="gene")
nucAPAinTot_SIG_egene=nucAPAinTot_SIG %>% semi_join(allEgenes, by="gene")

Proportion nucspec egene

nrow(nucAPAinTot_NOTSIG_egene)/nrow(nucAPAinTot_NOTSIG)
[1] 0.2430556
nrow(nucAPAinTot_SIG_egene)/nrow(nucAPAinTot_SIG)
[1] 0.2539326

Difference of proportion test:

prop.test(x=c(nrow(nucAPAinTot_NOTSIG_egene),nrow(nucAPAinTot_SIG_egene)),n=c(nrow(nucAPAinTot_NOTSIG),nrow(nucAPAinTot_SIG)))

    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(nucAPAinTot_NOTSIG_egene), nrow(nucAPAinTot_SIG_egene)) out of c(nrow(nucAPAinTot_NOTSIG), nrow(nucAPAinTot_SIG))
X-squared = 0.022815, df = 1, p-value = 0.8799
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.09636424  0.07461018
sample estimates:
   prop 1    prop 2 
0.2430556 0.2539326

Write out nuc specific QTLs

write.table(nucAPAinTot_NOTSIG,file="../data/QTLoverlap/NucSpecApaQTL.txt", quote=F, col.names = T, row.names = F )

Run this the opposite direction:

Total specific:

totAPAinNuc=read.table("../data/QTLoverlap/TotalQTLinNuclearNominal.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope")) %>% separate(peakID,into=c("chr", "start", "end", "geneID"),sep=":" ) %>% separate(geneID, into=c("gene", "loc", "strand", "peakNum"), sep="_")
totAPAinNuc_NOTSIG=totAPAinNuc %>% filter(pval>.05)
totAPAinNuc_SIG=totAPAinNuc %>% filter(pval<.05)
totAPAinNuc_NOTSIG_egene=totAPAinNuc_NOTSIG %>% semi_join(allEgenes, by="gene")
totAPAinNuc_SIG_egene=totAPAinNuc_SIG %>% semi_join(allEgenes, by="gene")

Proportion nucspec egene

nrow(totAPAinNuc_NOTSIG_egene)/nrow(totAPAinNuc_NOTSIG)
[1] 0.173913
nrow(totAPAinNuc_SIG_egene)/nrow(totAPAinNuc_SIG)
[1] 0.2818991
prop.test(x=c(nrow(totAPAinNuc_NOTSIG_egene),nrow(totAPAinNuc_SIG_egene)),n=c(nrow(totAPAinNuc_NOTSIG),nrow(totAPAinNuc_SIG)))

    2-sample test for equality of proportions with continuity
    correction

data:  c(nrow(totAPAinNuc_NOTSIG_egene), nrow(totAPAinNuc_SIG_egene)) out of c(nrow(totAPAinNuc_NOTSIG), nrow(totAPAinNuc_SIG))
X-squared = 2.9071, df = 1, p-value = 0.08819
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.218234371  0.002262238
sample estimates:
   prop 1    prop 2 
0.1739130 0.2818991 

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] workflowr_1.3.0 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 labeling_0.3    
[45] stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1     
[49] crayon_1.3.4