Usage Effect Sizes

Last updated: 2019-06-13

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/Readdistagainstfeatures.Rmd
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    Modified:   code/BothFracDTPlotGeneRegions.sh
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    Modified:   code/apaQTLCorrectPvalMakeQQ.R
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    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Deleted:    code/test.txt

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File	Version	Author	Date	Message
Rmd	968cb4b	brimittleman	2019-06-13	wflow_publish(“analysis/nonNormQTL.Rmd”)
html	230bc6a	brimittleman	2019-06-11	Build site.
Rmd	cd24c16	brimittleman	2019-06-11	new genos
html	5a31f4d	brimittleman	2019-05-28	Build site.
Rmd	6ece235	brimittleman	2019-05-28	add example code
html	de2aa7e	brimittleman	2019-05-28	Build site.
Rmd	c04929f	brimittleman	2019-05-28	add example code
html	f4a2106	brimittleman	2019-05-28	Build site.
Rmd	f10e64d	brimittleman	2019-05-28	add plot grid
html	64a7d5d	brimittleman	2019-05-28	Build site.
Rmd	f5260bd	brimittleman	2019-05-28	add results
html	28c8ca3	brimittleman	2019-05-24	Build site.
Rmd	3f63045	brimittleman	2019-05-24	add code to prepare non norm qtl

In order to compare effect sizes for the QTLs I have previously identified in an interpretable manner, I need to run the linear model with the non normalized usage. To do this I will separate the the usage (with annotation) files by chromosome and run fastqtl on these files.

library(workflowr)

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

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(cowplot)


Attaching package: 'cowplot'

The following object is masked from 'package:ggplot2':

    ggsave

Prepare files

countsnum= APApeak_Phenotype_GeneLocAnno.Total.5perc.CountsNumeric, APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.CountsNumeric

id file= APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz, APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz

totAnno= read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz", stringsAsFactors = F, header = T) %>% separate(chrom, into=c("Chrchrom", "Start", "End", "ID"),sep=":") %>% mutate(Chr=str_sub(Chrchrom, 4, str_length(Chrchrom)))
                                                                                                                                                                                                                
colnamesTot= colnames(totAnno)[5:58]
totUsage=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.CountsNumeric", stringsAsFactors = F, header = F, col.names = colnamesTot) 

totUsageAnno=as.data.frame(cbind(Chr=totAnno$Chr, start=totAnno$Start, end=totAnno$End, ID=totAnno$ID, totUsage ))

write.table(totUsageAnno,file="../data/nonNorm_pheno/TotalUsageAllChrom.txt", col.names = T, row.names = F, quote = F, sep="\t" )

nucAnno= read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz", stringsAsFactors = F, header = T)%>% separate(chrom, into=c("Chrchrom", "Start", "End", "ID"),sep=":") %>% mutate(Chr=str_sub(Chrchrom, 4, str_length(Chrchrom)))
colnamesNuc= colnames(nucAnno)[5:58]
nucUsage=read.table("../data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.CountsNumeric", stringsAsFactors = F, header = F, col.names = colnamesNuc) 


nucUsageAnno=as.data.frame(cbind(Chr=nucAnno$Chr, start=nucAnno$Start, end=nucAnno$End, ID=nucAnno$ID, nucUsage ))

write.table(nucUsageAnno,file="../data/nonNorm_pheno/NuclearUsageAllChrom.txt", col.names = T, row.names = F, quote = F, sep="\t" )

Run QTL scripts

I will create a python script to seperate the file into each chromosome for running fastQTL.

sbatch run_sepUsagephen.sh
sbatch ZipandTabPheno.sh
sbatch ApaQTL_nominalNonnorm.sh

Concatinate files:

cat  ../data/nonNorm_pheno/TotalUsageChrom*.nominal.out > ../data/nonNorm_pheno/TotalUsageChrom_Nominal_AllChrom.txt
cat ../data/nonNorm_pheno/NuclearUsageChrom*.nominal.out > ../data/nonNorm_pheno/NuclearUsageChrom_Nominal_AllChrom.txt

Pull out real total and nuc QLTs

python qtlsPvalOppFrac.py ../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt ../data/nonNorm_pheno/TotalUsageChrom_Nominal_AllChrom.txt ../data/QTLoverlap_nonNorm/NuclearQTLinTotalNominal_nonNorm.txt  

python qtlsPvalOppFrac.py ../data/apaQTLs/Nuclear_apaQTLs4pc_5fdr.txt ../data/nonNorm_pheno/NuclearUsageChrom_Nominal_AllChrom.txt ../data/QTLoverlap_nonNorm/NuclearQTLinNuclearNominal_nonNorm.txt  


python qtlsPvalOppFrac.py ../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt ../data/nonNorm_pheno/TotalUsageChrom_Nominal_AllChrom.txt ../data/QTLoverlap_nonNorm/TotalQTLinTotalNominal_nonNorm.txt  

python qtlsPvalOppFrac.py ../data/apaQTLs/Total_apaQTLs4pc_5fdr.txt ../data/nonNorm_pheno/NuclearUsageChrom_Nominal_AllChrom.txt ../data/QTLoverlap_nonNorm/TotalQTLinNuclearNominal_nonNorm.txt

totAPAinNuc=read.table("../data/QTLoverlap_nonNorm/TotalQTLinNuclearNominal_nonNorm.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope"))


nucAPAinTot=read.table("../data/QTLoverlap_nonNorm/NuclearQTLinTotalNominal_nonNorm.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope"))

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

Total

TotBoth= totAPAinNuc %>% inner_join(totAPAinTot,by=c("peakID", "snp"))

summary(lm(TotBoth$slope ~ TotBoth$Originalslope))


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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.67522 -0.03343 -0.00022  0.03521  0.36113 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)           -0.001467   0.004703  -0.312    0.755    
TotBoth$Originalslope  0.654634   0.024603  26.608   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.09434 on 404 degrees of freedom
Multiple R-squared:  0.6367,    Adjusted R-squared:  0.6358 
F-statistic:   708 on 1 and 404 DF,  p-value: < 2.2e-16

totbothplot=ggplot(TotBoth, aes(x=Originalslope, y=slope))+geom_point() + geom_smooth(method="lm") + labs(title="Total apaQTL effect sizes", x="Effect size in Total",y="Effect size in Nucler") + geom_density_2d(col="red") + annotate("text", y=1, x=0, label="R2=.64, slope=0.65")

Nuclear

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


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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.43316 -0.03363  0.00088  0.02970  0.36456 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)           0.0004951  0.0028911   0.171    0.864    
NucBoth$Originalslope 0.7098391  0.0165009  43.018   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.07032 on 591 degrees of freedom
Multiple R-squared:  0.7579,    Adjusted R-squared:  0.7575 
F-statistic:  1851 on 1 and 591 DF,  p-value: < 2.2e-16

Nucbothplot=ggplot(NucBoth, aes(x=Originalslope, y=slope))+geom_point() + geom_smooth(method="lm") + labs(title="Nuclear apaQTL effect sizes", x="Effect size in Nuclear",y="Effect size in Total") + geom_density_2d(col="red") +  annotate("text", y=1, x=0, label="R2=.76, slope=0.71")

plot_grid(totbothplot,Nucbothplot)

Version	Author	Date
230bc6a	brimittleman	2019-06-11
de2aa7e	brimittleman	2019-05-28
f4a2106	brimittleman	2019-05-28

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] cowplot_0.9.4   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 workflowr_1.3.0

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    MASS_7.3-51.1    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