Usage Effect Sizes

Last updated: 2019-09-04

Checks: 6 1

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:    docs/figure/nonNormQTL.Rmd/figure2D-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	3875f36	brimittleman	2019-08-01	Build site.
Rmd	3d49e9f	brimittleman	2019-08-01	figure2D
html	4dd2bde	brimittleman	2019-08-01	Build site.
Rmd	4fd345d	brimittleman	2019-08-01	figure2D
html	6757ce6	brimittleman	2019-07-17	Build site.
Rmd	bf42ee6	brimittleman	2019-07-17	remove density
html	4297ae4	brimittleman	2019-07-03	Build site.
Rmd	2cafaf1	brimittleman	2019-07-03	include spec qtl
html	0c7c682	brimittleman	2019-06-13	Build site.
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 1.4.0
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)[5:56]
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)[5:56]
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 
-0.66101 -0.03117  0.00361  0.03873  0.37205 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)           -0.004458   0.004247   -1.05    0.295    
TotBoth$Originalslope  0.638860   0.022554   28.33   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.0852 on 401 degrees of freedom
Multiple R-squared:  0.6668,    Adjusted R-squared:  0.6659 
F-statistic: 802.4 on 1 and 401 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=.67, slope=0.64")

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 
-0.23354 -0.03332  0.00028  0.03182  0.40042 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)           0.001628   0.002910   0.559    0.576    
NucBoth$Originalslope 0.775971   0.017539  44.243   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.06408 on 483 degrees of freedom
Multiple R-squared:  0.8021,    Adjusted R-squared:  0.8017 
F-statistic:  1957 on 1 and 483 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=.8, slope=0.78")

plot_grid(totbothplot,Nucbothplot)

Version	Author	Date
0c7c682	brimittleman	2019-06-13
230bc6a	brimittleman	2019-06-11
de2aa7e	brimittleman	2019-05-28
f4a2106	brimittleman	2019-05-28

For the nuclear plot I want to include the PAS we cannot test in the total fraction. To do this I will write code that only writes out the lines for the PAS not in the total fraction. I need the nominal effect sizes for the nuclear qtls in PAS we could not test in total. First I will get all of the PAS tested in total and figure out the nuclear QTLs not in this set. I will then pull those associations out of the nominal nuclear set.

python nucSpeceffectsize.py

nucSpec=read.table("../data/QTLoverlap_nonNorm/NuclearSpecQTLinNuclearNominal_nonNorm.txt",header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "Originalslope")) %>% mutate(slope=0,set="Specific") %>% select(peakID, snp, dist, pval, Originalslope, slope,set)
NucBoth_set= NucBoth %>% mutate(set="Both")
NucBothwSpec=bind_rows(NucBoth_set, nucSpec)

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


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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.29479 -0.03377  0.00364  0.03780  0.34739 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                -0.009236   0.002974  -3.106  0.00199 ** 
NucBothwSpec$Originalslope  0.664058   0.018388  36.114  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.07255 on 600 degrees of freedom
Multiple R-squared:  0.6849,    Adjusted R-squared:  0.6844 
F-statistic:  1304 on 1 and 600 DF,  p-value: < 2.2e-16

cor.test(y=NucBothwSpec$slope, x=NucBothwSpec$Originalslope)$p.value

[1] 1.336923e-152

nucspecplot=ggplot(NucBothwSpec, aes(x=Originalslope, y=slope))+geom_point(aes( col=set)) + geom_smooth(method="lm") + labs(title="Nuclear apaQTL effect sizes", x="Effect size in Nuclear",y="Effect size in Total") +  annotate("text", y=1, x=0, label="R2=.68, slope=0.66",size=6) + scale_color_brewer(palette = "Dark2")+ theme(text = element_text(size=16), legend.position = "bottom")

Do this in the other fraction to see what is looks like.

python totSeceffectsize.py

totSpec=read.table("../data/QTLoverlap_nonNorm/TotalSpecQTLinTotalNominal_nonNorm.txt",header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "Originalslope")) %>% mutate(slope=0,set="Specific") %>% select(peakID, snp, dist, pval, Originalslope, slope,set)
TotBoth_set= TotBoth %>% mutate(set="Both")
TotBothwSpec=bind_rows(TotBoth_set, totSpec)

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


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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.64645 -0.03081  0.00337  0.03800  0.37917 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)                -0.006902   0.003969  -1.739   0.0827 .  
TotBothwSpec$Originalslope  0.621773   0.021776  28.553   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.08339 on 441 degrees of freedom
Multiple R-squared:  0.649, Adjusted R-squared:  0.6482 
F-statistic: 815.2 on 1 and 441 DF,  p-value: < 2.2e-16

totspecplot=ggplot(TotBothwSpec, aes(x=Originalslope, y=slope))+geom_point(aes( col=set)) + geom_smooth(method="lm") + labs(title="Total apaQTL effect sizes", x="Effect size in Total",y="Effect size in Nuclear") +  annotate("text", y=1, x=0, label="R2=.65, slope=0.62", size=6) + scale_color_brewer(palette = "Dark2") + theme(text = element_text(size=16), legend.position = "bottom")

totspecplot

Version	Author	Date
6757ce6	brimittleman	2019-07-17

grideffectplot=plot_grid(nucspecplot,totspecplot)
grideffectplot

Version	Author	Date
6757ce6	brimittleman	2019-07-17
4297ae4	brimittleman	2019-07-03

TotBothwSpec %>% filter(set=="Specific") %>% nrow()

[1] 40

NucBothwSpec %>% filter(set=="Specific") %>% nrow()

[1] 117

prop.test(x=c(117,40), n=c(565,378), alternative ="greater")


    2-sample test for equality of proportions with continuity
    correction

data:  c(117, 40) out of c(565, 378)
X-squared = 16.012, df = 1, p-value = 3.147e-05
alternative hypothesis: greater
95 percent confidence interval:
 0.06079568 1.00000000
sample estimates:
   prop 1    prop 2 
0.2070796 0.1058201

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_1.4.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0         RColorBrewer_1.1-2 cellranger_1.1.0  
 [4] plyr_1.8.4         compiler_3.5.1     pillar_1.3.1      
 [7] git2r_0.25.2       highr_0.7          tools_3.5.1       
[10] digest_0.6.18      lubridate_1.7.4    jsonlite_1.6      
[13] evaluate_0.12      nlme_3.1-137       gtable_0.2.0      
[16] lattice_0.20-38    pkgconfig_2.0.2    rlang_0.4.0       
[19] cli_1.1.0          rstudioapi_0.10    yaml_2.2.0        
[22] haven_1.1.2        withr_2.1.2        xml2_1.2.0        
[25] httr_1.3.1         knitr_1.20         hms_0.4.2         
[28] generics_0.0.2     fs_1.3.1           rprojroot_1.3-2   
[31] grid_3.5.1         tidyselect_0.2.5   glue_1.3.0        
[34] R6_2.3.0           readxl_1.1.0       rmarkdown_1.10    
[37] modelr_0.1.2       magrittr_1.5       whisker_0.3-2     
[40] MASS_7.3-51.1      backports_1.1.2    scales_1.0.0      
[43] htmltools_0.3.6    rvest_0.3.2        assertthat_0.2.0  
[46] colorspace_1.3-2   labeling_0.3       stringi_1.2.4     
[49] lazyeval_0.2.1     munsell_0.5.0      broom_0.5.1       
[52] crayon_1.3.4