Last updated: 2019-09-04

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecAPAqtl.Rmd
    Modified:   analysis/PrematureTermQTL.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Modified:   code/SnakefilefiltPAS
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Modified:   code/mergeAllBam.sh
    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
    Modified:   code/peakFC.sh
    Modified:   code/snakemake.batch
    Modified:   code/snakemakePAS.batch
    Modified:   code/snakemakefiltPAS.batch
    Modified:   code/submit-snakemake.sh
    Modified:   code/submit-snakemakePAS.sh
    Modified:   code/submit-snakemakefiltPAS.sh
    Deleted:    code/test.txt
    Modified:   data/MetaDataSequencing.txt
    Modified:   docs/figure/QTLlocation.Rmd/figure2B-1.pdf
    Modified:   docs/figure/QTLlocation.Rmd/figure2C-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
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 1.4.0
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
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 apaQTL effect sizes", x="Effect size in Nuclear",y="Effect size in Total") + 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).

Version Author Date
8e8e51c brimittleman 2019-06-13
c4358f6 brimittleman 2019-06-11
d117338 brimittleman 2019-05-20
a88eedf brimittleman 2019-05-20
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",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).

Version Author Date
8e8e51c brimittleman 2019-06-13
c4358f6 brimittleman 2019-06-11
d117338 brimittleman 2019-05-20
a88eedf brimittleman 2019-05-20

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] qvalue_2.14.0   workflowr_1.4.0 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      pillar_1.3.1     glue_1.3.0       withr_2.1.2     
[13] modelr_0.1.2     readxl_1.1.0     plyr_1.8.4       munsell_0.5.0   
[17] gtable_0.2.0     cellranger_1.1.0 rvest_0.3.2      evaluate_0.12   
[21] labeling_0.3     knitr_1.20       highr_0.7        broom_0.5.1     
[25] Rcpp_1.0.0       scales_1.0.0     backports_1.1.2  jsonlite_1.6    
[29] fs_1.3.1         hms_0.4.2        digest_0.6.18    stringi_1.2.4   
[33] grid_3.5.1       rprojroot_1.3-2  cli_1.1.0        tools_3.5.1     
[37] magrittr_1.5     lazyeval_0.2.1   crayon_1.3.4     whisker_0.3-2   
[41] pkgconfig_2.0.2  MASS_7.3-51.1    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