Condition on Types ApaQTLs

Last updated: 2018-10-02

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Unstaged changes:
    Modified:   analysis/28ind.peak.explore.Rmd
    Modified:   analysis/PeakToGeneAssignment.Rmd
    Modified:   analysis/callMolQTLS.Rmd
    Modified:   analysis/cleanupdtseq.internalpriming.Rmd
    Modified:   analysis/dif.iso.usage.leafcutter.Rmd
    Modified:   analysis/diff_iso_pipeline.Rmd
    Modified:   analysis/explore.filters.Rmd
    Modified:   analysis/overlapMolQTL.Rmd
    Modified:   analysis/overlap_qtls.Rmd
    Modified:   analysis/peakOverlap_oppstrand.Rmd
    Modified:   analysis/pheno.leaf.comb.Rmd
    Modified:   analysis/test.max2.Rmd
    Modified:   code/Snakefile

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File	Version	Author	Date	Message
Rmd	f66679b	Briana Mittleman	2018-10-02	overlap plots at peak level

In this analysis I want to use the resuls from the total and nuclear APA qtl calling. I will ask if conditioning on a nuclear QTL increases the signal in the total QTL and vice versa. I will start with the significant snp-peak pairs from the permuted files. I will then overlap with the nominal pvalues from the other fraction. I will do this similar to how I did in the overlaMolQTL analysis. However in this analysis I do not have the multiple peaks per gene problem that I have when I overlap the pvalues. I can map the same peak to snp pair.

Due to file size I will do this only with the permuted files.

Load Libraries

library(tidyverse)

── Attaching packages ──────────────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
✔ tidyr   0.8.1     ✔ stringr 1.3.1
✔ readr   1.1.1     ✔ 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 1.1.1
Run ?workflowr for help getting started

Load Data

Permuted

permTot=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_transcript_permResBH.txt",header=T ,stringsAsFactors = F)
permNuc=read.table("../data/perm_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt",header=T, stringsAsFactors = F)

Nominal

nomnames=c("pid", "sid", "dist", "npval", "slope")
#nomTot=read_table("../data/nom_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_NomRes.txt", col_names=nomnames, col_types = c(col_character(), col_character(), col_double(), col_double(), col_double()))
#nomNuc=read_table("../data/nom_QTL_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_NomRes.txt", col_names=nomnames, col_types = c(col_character(), col_character(), col_double(), col_double(), col_double()))

Make QQplots

overlapQTLplot_totalQTL=function(cut, plotfile){

    #helper functions  
    sigsnp=function(cutoff){
      permTot$bh=p.adjust(permTot$bpval, method="fdr")
      file_sig=permTot %>% filter(-log10(bh)> cutoff) %>% select(sid)
      print(paste("Sig snps=", nrow(file_sig), sep=" "))
      return(file_sig)
    }
    randomsnps=function(SigSnpList){
      nsnp=nrow(SigSnpList)
      randomSnpDF= permTot %>% sample_n(nsnp) %>% arrange(sid) %>% select(sid)
      return(randomSnpDF)
    }
    top_Nuclear=function(snp_list){
      filt_nuc=permNuc %>% semi_join(snp_list, by="sid") %>% group_by(sid) %>% add_tally() %>% ungroup() %>% mutate(corrPval=bpval)
      filt_nuc_top= filt_nuc %>% group_by(sid) %>% top_n(-1, corrPval)
      print(paste("Nuclear overlap=", nrow(filt_nuc_top), sep=" "))
      return(filt_nuc_top)
    }
    makeQQ=function(test, baseline){
      plot=qqplot(-log10(runif(nrow(baseline))), -log10(baseline$corrPval), ylab="Observed", xlab="Expected", main="Significant Total QTLs- nuclear Pval")
      points(sort(-log10(runif(nrow(test)))), sort(-log10(test$corrPval)), col= alpha("Red"))
      abline(0,1)
      return(plot)
    }
  
    TL=sigsnp(cut)
    BL=randomsnps(TL)
    #top snps test and base total
    topN_T=top_Nuclear(TL)
    topN_B=top_Nuclear(BL)
    #plot Total
    png(plotfile)
    totalPlot=makeQQ(topN_T,topN_B)
    dev.off()

}

overlapQTLplot_totalQTL(1, "../output/plots/TotalQTLinNuclear.png")

[1] "Sig snps= 677"
[1] "Nuclear overlap= 250"
[1] "Nuclear overlap= 147"

quartz_off_screen 
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overlapQTLplot_totalQTL=function(cut, plotfile){

    #helper functions  
    sigsnp=function(cutoff){
      permNuc$bh=p.adjust(permNuc$bpval, method="fdr")
      file_sig=permNuc %>% filter(-log10(bh)> cutoff) %>% select(sid)
      print(paste("Sig snps=", nrow(file_sig), sep=" "))
      return(file_sig)
    }
    randomsnps=function(SigSnpList){
      nsnp=nrow(SigSnpList)
      randomSnpDF= permNuc %>% sample_n(nsnp) %>% arrange(sid) %>% select(sid)
      return(randomSnpDF)
    }
    top_Total=function(snp_list){
      filt_tot=permTot %>% semi_join(snp_list, by="sid") %>% group_by(sid) %>% add_tally() %>% ungroup() %>% mutate(corrPval=bpval)
      filt_tot_top= filt_tot %>% group_by(sid) %>% top_n(-1, corrPval)
      print(paste("Total overlap=", nrow(filt_tot_top), sep=" "))
      return(filt_tot_top)
    }
    makeQQ=function(test, baseline){
      plot=qqplot(-log10(runif(nrow(baseline))), -log10(baseline$corrPval), ylab="Observed", xlab="Expected", main="Significant Nuclear QTLs- Total Pval")
      points(sort(-log10(runif(nrow(test)))), sort(-log10(test$corrPval)), col= alpha("Red"))
      abline(0,1)
      return(plot)
    }
  
    TL=sigsnp(cut)
    BL=randomsnps(TL)
    #top snps test and base total
    topN_T=top_Total(TL)
    topN_B=top_Total(BL)
    #plot Total
    png(plotfile)
    totalPlot=makeQQ(topN_T,topN_B)
    dev.off()

}
overlapQTLplot_totalQTL(1, "../output/plots/NuclearQTLinTotal.png")

[1] "Sig snps= 3049"
[1] "Total overlap= 689"
[1] "Total overlap= 561"

quartz_off_screen 
                2

I should change this to focus on peak. I can say give me the genes with significant QTLs in total or nuclear then look at the pvalues for those peaks in the other file. As I did before, I am going to work on all of the functions seperatly then put them together.

Get the peaks with significant QTLs, and the same number of random peaks.

sigpeak=function(cutoff){
    permNuc$bh=p.adjust(permNuc$bpval, method="fdr")
    file_sig=permNuc %>% filter(-log10(bh)> cutoff) %>%separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(peak)
    print(paste("Sig peaks=", nrow(file_sig), sep=" "))
    return(file_sig)
    }
    
x=sigpeak(1)

randompeak=function(SigSnpList){
  nsnp=nrow(SigSnpList)
  randomPeakDF= permNuc %>% sample_n(nsnp) %>% separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(peak)
  return(randomPeakDF)
}

y=randompeak(x)

I can now get the top pvalue for each of these using the total permuted pval.

Peak_overlap=function(snp_list){
      filt_tot=permTot %>% separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% semi_join(snp_list, by="peak")
      print(paste("Total overlap=", nrow(filt_tot), sep=" "))
      return(filt_tot)
}


#run on real sig peaks and random peaks  
Test=Peak_overlap(x)
base=Peak_overlap(y)

Plot:

  makeQQ_peak=function(test, baseline){
      plot=qqplot(-log10(runif(nrow(test))), -log10(test$bpval), ylab="Observed", xlab="Expected", main="Peaks with Significant QTLs in Nuc \n pvalues in Tot")
      points(sort(-log10(runif(nrow(baseline)))), sort(-log10(baseline$bpval)), col=alpha("Red"))
      abline(0,1)
      return(plot)
}
plot=makeQQ_peak(Test,base)

SigNucPeakOverlapTot=function(cutoff, plotfile){
    sigpeak=function(cutoff){
      permNuc$bh=p.adjust(permNuc$bpval, method="fdr")
      file_sig=permNuc %>% filter(-log10(bh)> cutoff) %>%separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(peak)
      print(paste("Sig peaks=", nrow(file_sig), sep=" "))
      return(file_sig)
      }
  
  randompeak=function(SigSnpList){
    nsnp=nrow(SigSnpList)
    randomPeakDF= permNuc %>% sample_n(nsnp) %>% separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(peak)
    return(randomPeakDF)
  }
  Peak_overlap=function(snp_list){
        filt_tot=permTot %>% separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% semi_join(snp_list, by="peak")
        print(paste("Total overlap=", nrow(filt_tot), sep=" "))
        return(filt_tot)
  }

  makeQQ_peak=function(test, baseline){
      plot=qqplot(-log10(runif(nrow(baseline))), -log10(baseline$bpval), ylab="Observed", xlab="Expected", main="Peaks with Significant QTLs in Nuclear \n pvalues in Total")
      points(sort(-log10(runif(nrow(test)))), sort(-log10(test$bpval)), col= alpha("Red"))
      abline(0,1)
      return(plot)
  }
  
  
testPeaks=sigpeak(1)
basePeaks=randompeak(testPeaks)
testSet=Peak_overlap(testPeaks)
baselineSet=Peak_overlap(basePeaks)
png(plotfile)
plot=makeQQ_peak(testSet,baselineSet )
dev.off()
}

SigNucPeakOverlapTot(1, "../output/plots/SigNucPeakTotpval.png")

[1] "Sig peaks= 3049"
[1] "Total overlap= 2352"
[1] "Total overlap= 2369"

quartz_off_screen 
                2

SigTotPeakOverlapNuc=function(cutoff, plotfile){
    sigpeak=function(cutoff){
      permTot$bh=p.adjust(permTot$bpval, method="fdr")
      file_sig=permTot %>% filter(-log10(bh)> cutoff) %>%separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(peak)
      print(paste("Sig peaks=", nrow(file_sig), sep=" "))
      return(file_sig)
      }
  
  randompeak=function(SigSnpList){
    nsnp=nrow(SigSnpList)
    randomPeakDF= permTot %>% sample_n(nsnp) %>% separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% select(peak)
    return(randomPeakDF)
  }
  Peak_overlap=function(snp_list){
        filt_nuc=permNuc %>% separate(pid, sep = ":", into=c("chr", "start", "end", "id")) %>% separate(id, sep = "_", into=c("gene", "strand", "peak")) %>% semi_join(snp_list, by="peak")
        print(paste("Nuclear overlap=", nrow(filt_nuc), sep=" "))
        return(filt_nuc)
  }

  makeQQ_peak=function(test, baseline){
      plot=qqplot(-log10(runif(nrow(baseline))), -log10(baseline$bpval), ylab="Observed", xlab="Expected", main="Peaks with Significant QTLs in Total \n pvalues in Nuclear")
      points(sort(-log10(runif(nrow(test)))), sort(-log10(test$bpval)), col= alpha("Red"))
      abline(0,1)
      return(plot)
  }
  
  
testPeaks=sigpeak(1)
basePeaks=randompeak(testPeaks)
testSet=Peak_overlap(testPeaks)
baselineSet=Peak_overlap(basePeaks)
png(plotfile)
plot=makeQQ_peak(testSet,baselineSet )
dev.off()
}

SigTotPeakOverlapNuc(1, "../output/plots/SigTotPeakNucpval.png")

[1] "Sig peaks= 677"
[1] "Nuclear overlap= 574"
[1] "Nuclear overlap= 527"

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```

Session information

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] bindrcpp_0.2.2  workflowr_1.1.1 reshape2_1.4.3  forcats_0.3.0  
 [5] stringr_1.3.1   dplyr_0.7.6     purrr_0.2.5     readr_1.1.1    
 [9] tidyr_0.8.1     tibble_1.4.2    ggplot2_3.0.0   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4  haven_1.1.2       lattice_0.20-35  
 [4] colorspace_1.3-2  htmltools_0.3.6   yaml_2.2.0       
 [7] rlang_0.2.2       R.oo_1.22.0       pillar_1.3.0     
[10] glue_1.3.0        withr_2.1.2       R.utils_2.7.0    
[13] modelr_0.1.2      readxl_1.1.0      bindr_0.1.1      
[16] plyr_1.8.4        munsell_0.5.0     gtable_0.2.0     
[19] cellranger_1.1.0  rvest_0.3.2       R.methodsS3_1.7.1
[22] evaluate_0.11     knitr_1.20        broom_0.5.0      
[25] Rcpp_0.12.18      scales_1.0.0      backports_1.1.2  
[28] jsonlite_1.5      hms_0.4.2         digest_0.6.16    
[31] stringi_1.2.4     grid_3.5.1        rprojroot_1.3-2  
[34] cli_1.0.0         tools_3.5.1       magrittr_1.5     
[37] lazyeval_0.2.1    crayon_1.3.4      whisker_0.3-2    
[40] pkgconfig_2.0.2   xml2_1.2.0        lubridate_1.7.4  
[43] assertthat_0.2.0  rmarkdown_1.10    httr_1.3.1       
[46] rstudioapi_0.7    R6_2.2.2          nlme_3.1-137     
[49] git2r_0.23.0      compiler_3.5.1

This reproducible R Markdown analysis was created with workflowr 1.1.1

Condition on Types ApaQTLs

Briana Mittleman

10/2/2018

Make QQplots

Session information