Total and Nuclear QTL sharing

Last updated: 2020-03-23

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecIncludeNotTested.Rmd
    Modified:   analysis/PASdescriptiveplots.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/TSS.Rmd
    Modified:   analysis/apabyeQTLstatus.Rmd
    Modified:   analysis/decayAndStability.Rmd
    Modified:   analysis/miRNAdisrupt.Rmd
    Modified:   analysis/nascenttranscription.Rmd
    Modified:   analysis/nucSpecinEQTLs.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/pQTLexampleplot.Rmd
    Modified:   analysis/reads_graphs.Rmd
    Modified:   analysis/splicesitestrength.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/DistPAS2Sig.py
    Modified:   code/Script4NuclearQTLexamples.sh
    Modified:   code/Script4TotalQTLexamples.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/apaqtlfacetboxplots.R
    Modified:   code/environment.yaml
    Modified:   code/run_qtlFacetBoxplots.sh
    Deleted:    code/test.txt
    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
Rmd	efdc398	brimittleman	2020-03-23	plots for supp
html	7711ef9	brimittleman	2019-09-04	Build site.
Rmd	73f5833	brimittleman	2019-09-04	wflow_publish(c(“analysis/QTLlocation.Rmd”, “analysis/apaQTLoverlap.Rmd”, “analysis/nonNormQTL.Rmd”,
html	8e8e51c	brimittleman	2019-06-13	Build site.
html	c4358f6	brimittleman	2019-06-11	Build site.
html	d117338	brimittleman	2019-05-20	Build site.
Rmd	7da06f5	brimittleman	2019-05-20	switch log effect
html	a88eedf	brimittleman	2019-05-20	Build site.
Rmd	8f883d8	brimittleman	2019-05-20	add overlap analysis

This analysis will investigate the sharing between total and nuclear apaQTls first by calculating the pi1 statistic and second by looking at the correlation of effect sizes.

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(reshape2)


Attaching package: 'reshape2'

The following object is masked from 'package:tidyr':

    smiths

library(workflowr)

This is workflowr version 1.6.0
Run ?workflowr for help getting started

library(qvalue)

Pi 1 sharing

Concatinate nominal results and run

#concatinate nominal res

cat ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_chr* >../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChrom.txt

cat ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_chr* >../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt

mkdir ../data/QTLoverlap/
python qtlsPvalOppFrac.py ../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChrom.txt ../data/QTLoverlap/TotalQTLinNuclearNominal.txt  


python qtlsPvalOppFrac.py ../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt ../data/QTLoverlap/NuclearQTLinTotalNominal.txt

totAPAinNuc=read.table("../data/QTLoverlap/TotalQTLinNuclearNominal.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope"))
qval_tot=pi0est(totAPAinNuc$pval, pi0.method = "bootstrap")

nucAPAinTot=read.table("../data/QTLoverlap/NuclearQTLinTotalNominal.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope"))
qval_nuc=pi0est(nucAPAinTot$pval, pi0.method = "bootstrap")

par(mfrow=c(1,2))
hist(totAPAinNuc$pval, xlab="Nuclear Pvalue", main="Significant Total APA QTLs \n Nuclear")
text(.8,300, paste("pi_1=", round((1-qval_tot$pi0), digit=3), sep=" "))
hist(nucAPAinTot$pval, xlab="Total Pvalue", main="Significant Nuclear APA QTLs \n Total")
text(.8,350, paste("pi_1=", round((1-qval_nuc$pi0), digit=3), sep=" "))

Version	Author	Date
7711ef9	brimittleman	2019-09-04
8e8e51c	brimittleman	2019-06-13
c4358f6	brimittleman	2019-06-11
a88eedf	brimittleman	2019-05-20

Effect size sharing:

I need to get the nominal effect sizes. I can use the script I wrote above but put the same fraction in for the qtl and nom values.


python qtlsPvalOppFrac.py ../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_AllChrom.txt ../data/QTLoverlap/TotalQTLinTotalNominal.txt  


python qtlsPvalOppFrac.py ../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt ../data/apaQTLNominal_4pc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_AllChrom.txt ../data/QTLoverlap/NuclearQTLinNuclearNominal.txt

totAPAinTot=read.table("../data/QTLoverlap/TotalQTLinTotalNominal.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope")) %>% dplyr::select(peakID, snp, slope) %>% dplyr::rename("Originalslope"=slope)


nucAPAinNuc=read.table("../data/QTLoverlap/NuclearQTLinNuclearNominal.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope")) %>% dplyr::select(peakID, snp, slope)%>% dplyr::rename("Originalslope"=slope)

Join the data frames:

Total:

TotBoth= totAPAinNuc %>% inner_join(totAPAinTot,by=c("peakID", "snp"))
summary(lm(log10(TotBoth$slope) ~ log10(TotBoth$Originalslope)))

Warning in eval(predvars, data, env): NaNs produced

Warning in eval(predvars, data, env): NaNs produced


Call:
lm(formula = log10(TotBoth$slope) ~ log10(TotBoth$Originalslope))

Residuals:
     Min       1Q   Median       3Q      Max 
-1.98510 -0.09337  0.09067  0.16879  0.35206 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                  -0.17332    0.02196  -7.892 2.26e-13 ***
log10(TotBoth$Originalslope)  0.98512    0.13015   7.569 1.56e-12 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.3041 on 191 degrees of freedom
  (210 observations deleted due to missingness)
Multiple R-squared:  0.2307,    Adjusted R-squared:  0.2267 
F-statistic: 57.29 on 1 and 191 DF,  p-value: 1.556e-12

ggplot(TotBoth, aes(x=log10(Originalslope), y=log10(slope)))+geom_point() + geom_smooth(method="lm") + labs(title="Total mRNA apaQTL effect sizes", x="Effect size in Nuclear",y="Effect size in Total mRNA") + geom_density_2d(col="red")

Warning in FUN(X[[i]], ...): NaNs produced

Warning in FUN(X[[i]], ...): NaNs produced

Warning in FUN(X[[i]], ...): NaNs produced

Warning in FUN(X[[i]], ...): NaNs produced

Warning in FUN(X[[i]], ...): NaNs produced

Warning in FUN(X[[i]], ...): NaNs produced

Warning in FUN(X[[i]], ...): NaNs produced

Warning in FUN(X[[i]], ...): NaNs produced

Warning: Removed 210 rows containing non-finite values (stat_smooth).

Warning: Removed 210 rows containing non-finite values (stat_density2d).

Warning: Removed 210 rows containing missing values (geom_point).

NucBoth= nucAPAinTot %>% inner_join(nucAPAinNuc,by=c("peakID", "snp"))
summary(lm(log10(NucBoth$slope) ~ log10(NucBoth$Originalslope)))

Warning in eval(predvars, data, env): NaNs produced

Warning in eval(predvars, data, env): NaNs produced


Call:
lm(formula = log10(NucBoth$slope) ~ log10(NucBoth$Originalslope))

Residuals:
     Min       1Q   Median       3Q      Max 
-2.36694 -0.09634  0.07094  0.18822  0.43829 

Coefficients:
                             Estimate Std. Error t value Pr(>|t|)    
(Intercept)                  -0.17159    0.02105  -8.150 2.65e-14 ***
log10(NucBoth$Originalslope)  0.78145    0.13940   5.606 6.11e-08 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.3142 on 222 degrees of freedom
  (259 observations deleted due to missingness)
Multiple R-squared:  0.124, Adjusted R-squared:  0.1201 
F-statistic: 31.43 on 1 and 222 DF,  p-value: 6.113e-08

ggplot(NucBoth, aes(x=log10(Originalslope), y=log10(slope)))+geom_point() + geom_smooth(method="lm") + labs(title="Nuclear apaQTL effect sizes", x="Effect size in Total mRNA",y="Effect size in Nuclear") + geom_density_2d(col="red")

Warning in FUN(X[[i]], ...): NaNs produced

Warning in FUN(X[[i]], ...): NaNs produced

Warning in FUN(X[[i]], ...): NaNs produced

Warning in FUN(X[[i]], ...): NaNs produced

Warning in FUN(X[[i]], ...): NaNs produced

Warning in FUN(X[[i]], ...): NaNs produced

Warning in FUN(X[[i]], ...): NaNs produced

Warning in FUN(X[[i]], ...): NaNs produced

Warning: Removed 259 rows containing non-finite values (stat_smooth).

Warning: Removed 259 rows containing non-finite values (stat_density2d).

Warning: Removed 259 rows containing missing values (geom_point).

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] qvalue_2.14.0   workflowr_1.6.0 reshape2_1.4.3  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

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.5 splines_3.5.1    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] rlang_0.4.0      later_0.7.5      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       httpuv_1.4.5    
[25] broom_0.5.1      Rcpp_1.0.2       promises_1.0.1   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      magrittr_1.5     lazyeval_0.2.1  
[41] crayon_1.3.4     whisker_0.3-2    pkgconfig_2.0.2  MASS_7.3-51.1   
[45] xml2_1.2.0       lubridate_1.7.4  assertthat_0.2.0 rmarkdown_1.10  
[49] httr_1.3.1       rstudioapi_0.10  R6_2.3.0         nlme_3.1-137    
[53] git2r_0.26.1     compiler_3.5.1

Total and Nuclear QTL sharing

Briana Mittleman

5/20/2019