Nuclear specific apaQTLs

Last updated: 2019-09-06

Checks: 6 1

Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
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    Modified:   code/apaQTLCorrectPvalMakeQQ.R
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    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
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    Modified:   docs/figure/propeQTLs_explained.Rmd/figure3B-1.pdf
    Deleted:    reads_graphs.Rmd

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.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
html	acdd415	brimittleman	2019-06-19	Build site.
Rmd	3495996	brimittleman	2019-06-19	add loc of nuc spec
html	e03f9ab	brimittleman	2019-06-16	Build site.
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  
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── Conflicts ───────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(workflowr)

This is workflowr version 1.4.0
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)


shared=nucAPAinTot_SIG %>% select(-dist,-pval, -slope)
#write out shared qtls  

write.table(shared,"../data/apaQTLs/SharedAPAQTLs.txt",col.names = T, row.names = F, quote = F, sep="\t" )

hist(nucAPAinTot$pval, breaks=100)

Version	Author	Date
e03f9ab	brimittleman	2019-06-16

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

Version	Author	Date
e03f9ab	brimittleman	2019-06-16

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] 0.2307692

nrow(nucAPAinTot_SIG_egene)/nrow(nucAPAinTot_SIG)

[1] 0.2664908

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 = 0.37261, df = 1, p-value = 0.5416
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.13425064  0.06280757
sample estimates:
   prop 1    prop 2 
0.2307692 0.2664908

Write out nuc specific QTLs

write.table(nucAPAinTot_NOTSIG,file="../data/QTLoverlap/NucSpecApaQTL.txt", quote=F, col.names = T, row.names = F )
write.table(nucAPAinTot_SIG,file="../data/QTLoverlap/SharedApaQTL.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] 0.2195122

nrow(totAPAinNuc_SIG_egene)/nrow(totAPAinNuc_SIG)

[1] 0.2679128

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 = 0.56634, df = 1, p-value = 0.4517
alternative hypothesis: two.sided
95 percent confidence interval:
 -0.15790533  0.06110417
sample estimates:
   prop 1    prop 2 
0.2195122 0.2679128

Location of nuclear specific qtl pas:

nucAPAinTot_withsig=nucAPAinTot %>% mutate(NucSpec=ifelse(pval<.05, "No", "Yes"))

distribution:

ggplot(nucAPAinTot_withsig, aes(x=loc, fill=NucSpec)) + geom_bar()

Version	Author	Date
acdd415	brimittleman	2019-06-19

Proportion within category:

nucAPAinTot_withsig_group= nucAPAinTot_withsig %>% group_by(loc, NucSpec) %>% summarise(nNucSpec=n()) %>% ungroup() %>% group_by(loc) %>% mutate(nLoc=sum(nNucSpec)) %>% ungroup() %>% mutate(prop=nNucSpec/nLoc)


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

Version	Author	Date
acdd415	brimittleman	2019-06-19

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_1.4.0 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 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.1     git2r_0.25.2     highr_0.7        tools_3.5.1     
 [9] digest_0.6.18    lubridate_1.7.4  jsonlite_1.6     evaluate_0.12   
[13] nlme_3.1-137     gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2 
[17] rlang_0.4.0      cli_1.1.0        rstudioapi_0.10  yaml_2.2.0      
[21] haven_1.1.2      withr_2.1.2      xml2_1.2.0       httr_1.3.1      
[25] knitr_1.20       hms_0.4.2        generics_0.0.2   fs_1.3.1        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.10   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2
[45] labeling_0.3     stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0   
[49] broom_0.5.1      crayon_1.3.4

Nuclear specific apaQTLs

Briana Mittleman

6/14/2019