Last updated: 2019-05-28

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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 ──
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── Conflicts ──────────────────────────────────────────────────── tidyverse_conflicts() ──
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✖ 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  TotalUsageChrom*.nominal.out > TotalUsageChrom_Nominal_AllChrom.txt
cat  NuclearUsageChrom*.nominal.out > 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 
-1.34029 -0.07935 -0.01337  0.06956  0.46743 

Coefficients:
                      Estimate Std. Error t value Pr(>|t|)    
(Intercept)           0.019095   0.006562    2.91  0.00376 ** 
TotBoth$Originalslope 0.398243   0.013884   28.68  < 2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1518 on 543 degrees of freedom
Multiple R-squared:  0.6024,    Adjusted R-squared:  0.6017 
F-statistic: 822.8 on 1 and 543 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=2, x=2, label="R2=.61, slope=0.4")

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 
-0.74544 -0.03953  0.00153  0.04010  0.71657 

Coefficients:
                       Estimate Std. Error t value Pr(>|t|)    
(Intercept)           -0.001065   0.003189  -0.334    0.738    
NucBoth$Originalslope  0.742666   0.011220  66.193   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.0998 on 1010 degrees of freedom
Multiple R-squared:  0.8127,    Adjusted R-squared:  0.8125 
F-statistic:  4381 on 1 and 1010 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=2, x=1, label="R2=.81, slope=0.74")
plot_grid(totbothplot,Nucbothplot)

Version Author Date
de2aa7e brimittleman 2019-05-28
f4a2106 brimittleman 2019-05-28

Remove Effect size > abs(1)

totAPAinNucFilt=read.table("../data/QTLoverlap_nonNorm/TotalQTLinNuclearNominal_nonNorm.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope")) %>% filter(abs(slope)<= 1)


nucAPAinTotFilt=read.table("../data/QTLoverlap_nonNorm/NuclearQTLinTotalNominal_nonNorm.txt", header = F, stringsAsFactors = F, col.names=c("peakID", "snp", "dist", "pval", "slope")) %>% filter(abs(slope)<= 1)

totAPAinTotFilt=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) %>%   filter(abs(slope)<= 1) %>% dplyr::rename("Originalslope"=slope)

nucAPAinNucFilt=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)%>%  filter(abs(slope)<= 1) %>%dplyr::rename("Originalslope"=slope)
TotBothFilt= totAPAinNucFilt %>% inner_join(totAPAinTotFilt,by=c("peakID", "snp"))

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

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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.43593 -0.04769 -0.00197  0.05133  0.48722 

Coefficients:
                          Estimate Std. Error t value Pr(>|t|)    
(Intercept)               0.004788   0.004634   1.033    0.302    
TotBothFilt$Originalslope 0.767422   0.018322  41.885   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.1063 on 542 degrees of freedom
Multiple R-squared:  0.764, Adjusted R-squared:  0.7635 
F-statistic:  1754 on 1 and 542 DF,  p-value: < 2.2e-16
totbothplotfilt=ggplot(TotBothFilt, aes(x=Originalslope, y=slope))+geom_point() + geom_smooth(method="lm") + labs(title="Total apaQTL effect sizes", y="Effect size in Nuclear",x="Effect size in Total") + geom_density_2d(col="red") + annotate("text", y=.75, x=.1, label="R2=.76, slope=0.77")+ geom_abline(slope=1,color="green")
NucBothFilt= nucAPAinTotFilt %>% inner_join(nucAPAinNucFilt,by=c("peakID", "snp"))
summary(lm(NucBothFilt$slope ~ NucBothFilt$Originalslope))

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

Residuals:
     Min       1Q   Median       3Q      Max 
-0.74819 -0.03867  0.00179  0.04084  0.37901 

Coefficients:
                            Estimate Std. Error t value Pr(>|t|)    
(Intercept)               -0.0006196  0.0030789  -0.201    0.841    
NucBothFilt$Originalslope  0.7059759  0.0133986  52.690   <2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Residual standard error: 0.09539 on 1007 degrees of freedom
Multiple R-squared:  0.7338,    Adjusted R-squared:  0.7336 
F-statistic:  2776 on 1 and 1007 DF,  p-value: < 2.2e-16
Nucbothplotfilt=ggplot(NucBothFilt, aes(x=Originalslope, y=slope))+geom_point() + geom_smooth(method="lm") + labs(title="Nuclear apaQTL effect sizes", y="Effect size in Total",x="Effect size in Nuclear") + geom_density_2d(col="red") +  annotate("text", y=.75, x=.1, label="R2=.73, slope=0.71")+ geom_abline(slope=1,color="green")
plot_grid(totbothplotfilt,Nucbothplotfilt)

Version Author Date
de2aa7e brimittleman 2019-05-28

Box plots to look at the outliers:

get headers:

less /project2/gilad/briana/YRI_geno_hg19/chr3.dose.filt.vcf.gz | head -n14 | tail -n1 > /project2/gilad/briana/apaQTL/data/ExampleQTLPlots/genotypeHeader.txt
less /project2/gilad/briana/apaQTL/data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz | head -n1 > /project2/gilad/briana/apaQTL/data/ExampleQTLPlots/phenotypeHeader.txt

remove the hashtag in these

apaqtlfacetboxplots.R

library(tidyverse)
library(reshape2)
library(optparse)
library(cowplot)


option_list = list(
  make_option(c("-P", "--pheno"), action="store", default=NA, type='character',
              help="input pheno file"),
  make_option(c("-G", "--geno"), action="store", default=NA, type='character',
              help="input genotype"),
  make_option(c("-g", "--gene"), action="store", default=NA, type='character',
              help="gene"),
  make_option(c("-p", "--peakID"), action="store", default=NA, type='character',
              help="peakID"),
  make_option(c("-o", "--output"), action="store", default=NA, type='character',
              help="output file for plot")
)

opt_parser <- OptionParser(option_list=option_list)
opt <- parse_args(opt_parser)


opt_parser <- OptionParser(option_list=option_list)
opt <- parse_args(opt_parser)


phenohead=read.table("/project2/gilad/briana/apaQTL/data/ExampleQTLPlots/phenotypeHeader.txt", header = T,stringsAsFactors = F)
pheno=read.table(opt$pheno, col.names =colnames(phenohead),stringsAsFactors = F)


meltpheno=melt(pheno, id.vars = "chrom", value.name = "Ratio", variable.name = "Individual") %>% separate(Ratio, into=c("num", "denom"), sep="/")  %>%   separate(chrom, into=c("chrom", "start", "end", "peakID"),sep=":") %>% mutate(PeakLoc=paste(start, end, sep=":"))

meltpheno$Individual= as.character(meltpheno$Individual)
meltpheno$num= as.numeric(meltpheno$num)
meltpheno$denom=as.numeric(meltpheno$denom)


genoHead=read.table("/project2/gilad/briana/apaQTL/data/ExampleQTLPlots/genotypeHeader.txt", header = T,stringsAsFactors = F)
geno=read.table(opt$geno, col.names =colnames(genoHead),stringsAsFactors = F ) %>% select(ID,contains("NA")) 


lettersGeno=read.table(opt$geno, col.names =colnames(genoHead),stringsAsFactors = F,colClasses = c("character")) %>% select(REF, ALT)

refAllele=lettersGeno$REF
altAllele=lettersGeno$ALT


genoMelt=melt(geno, id.vars = "ID", value.name = "FullGeno", variable.name = "Individual" ) %>% separate(FullGeno, into=c("geno","dose","extra1"), sep=":") %>% select(Individual, dose) %>% mutate(genotype=ifelse(round(as.integer(dose))==0, paste(refAllele, refAllele, sep=""), ifelse(round(as.integer(dose))==1, paste(refAllele,altAllele, sep=""), paste(altAllele,altAllele,sep=""))))
genoMelt$Individual= as.character(genoMelt$Individual)


pheno_qtlpeak=meltpheno %>% inner_join(genoMelt, by="Individual") %>%  mutate(PAU=num/denom) 

qtlplot=ggplot(pheno_qtlpeak, aes(x=genotype, y=PAU, fill=genotype)) + geom_boxplot(width=.5)+ geom_jitter(alpha=1) + facet_grid(~PeakLoc) +scale_fill_brewer(palette = "YlOrRd")

ggsave(plot=qtlplot, filename=opt$output, height=10, width=10)

Code for boxplots:

run_qtlFacetBoxplots.sh

#!/bin/bash

#SBATCH --job-name=qtlFacetBoxplots
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=qtlFacetBoxplots.out
#SBATCH --error=qtlFacetBoxplots.err
#SBATCH --partition=broadwl
#SBATCH --mem=18G
#SBATCH --mail-type=END


module load Anaconda3
source activate three-prime-env

Fraction=$1
gene=$2
chrom=$3
snp=$4
peakID=$5


less /project2/gilad/briana/apaQTL/data/phenotype_5perc/APApeak_Phenotype_GeneLocAnno.${Fraction}.5perc.fc.gz | grep ${gene}_ > /project2/gilad/briana/apaQTL/data/ExampleQTLPlots/${gene}_${Fraction}PeaksPheno.txt


less /project2/gilad/briana/YRI_geno_hg19/chr${chrom}.dose.filt.vcf.gz | grep ${snp} >  /project2/gilad/briana/apaQTL/data/ExampleQTLPlots/${gene}_${Fraction}PeaksGenotype.txt

Rscript apaqtlfacetboxplots.R -P /project2/gilad/briana/apaQTL/data/ExampleQTLPlots/${gene}_${Fraction}PeaksPheno.txt -G /project2/gilad/briana/apaQTL/data/ExampleQTLPlots/${gene}_${Fraction}PeaksGenotype.txt --gene ${gene} -p ${peakID}  -o /project2/gilad/briana/apaQTL/data/ExampleQTLPlots/${gene}_${Fraction}${SNP}${peakID}_boxplot.png

totAPAinTot %>% filter(abs(Originalslope)>1)
                        peakID         snp Originalslope
1      NBPF9_intron_-_peak7314 1:144701300       3.14235
2 HLA-DRB5_intron_+_peak113454  6:32486756    -207.01900
3    HLA-DRB6_end_+_peak113461  6:32538598       9.32331
sbatch run_qtlFacetBoxplots.sh "Total" "NBPF9" "1" "1:144701300" "peak7314"
sbatch run_qtlFacetBoxplots.sh "Total" "HLA-DRB5" "6" "6:32486756" "peak113454"
sbatch run_qtlFacetBoxplots.sh "Total" "HLA-DRB6" "6" "6:32538598" "peak113461"

nucAPAinNuc %>% filter(abs(Originalslope)>1)
                        peakID         snp Originalslope
1  LINC00869_intron_-_peak7883 1:149598905      -2.47583
2       FRG1BP_end_-_peak80905 20:29641550      -1.54150
3 HLA-DRB5_intron_+_peak113456  6:32468906       4.46867
sbatch run_qtlFacetBoxplots.sh "Nuclear" "LINC00869" "1" "1:149598905" "peak7883"
sbatch run_qtlFacetBoxplots.sh "Nuclear" "FRG1BP" "20" "20:29641550" "peak80905"
sbatch run_qtlFacetBoxplots.sh "Nuclear" "HLA-DRB5" "6" "6:32468906" "peak113456"

#test case  
sbatch run_qtlFacetBoxplots.sh "Nuclear" "TAF3" "10" "10:7980931" "peak14035"


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_0.23.0     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