Last updated: 2019-02-14

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    Modified:   analysis/swarmPlots_QTLs.Rmd
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    Modified:   code/Snakefile
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Expand here to see past versions:
    File Version Author Date Message
    Rmd d09198c Briana Mittleman 2019-02-14 expression and gene anno
    html 450b389 Briana Mittleman 2019-02-06 Build site.
    Rmd 6bac9f9 Briana Mittleman 2019-02-06 add distance plots for QC on APAqtls

I will use this to look at some metrics around the the QTLs from the pipeline for all 55 individuals. In this analysis I found 363 qtls in the total fraction and 623 in the nuclear.

library(workflowr)
This is workflowr version 1.1.1
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
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✔ readr   1.1.1     ✔ forcats 0.3.0
── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
totQTLs=read.table("../data/perm_QTL_GeneLocAnno_noMP_5percov/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_permResBH.txt", stringsAsFactors = F, header=T)%>% filter(-log10(bh)>=1)

write.table(totQTLs,"../data/ApaQTLs/TotalapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt", row.names = F, col.names = F, quote = F)

nucQTLs=read.table("../data/perm_QTL_GeneLocAnno_noMP_5percov/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_permResBH.txt", stringsAsFactors = F, header=T)%>% filter(-log10(bh)>=1)

write.table(nucQTLs,"../data/ApaQTLs/NuclearapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt", row.names = F, col.names = F, quote = F)

Distance to end of PAS

I want to look at the distance between the QTL snp and the end of a peak. For a positive strand gene this is the end of the peak, for a - strand gene this is the start position of the peak. The peak strand here is opposite of the strand the gene is on.

I will make a python script that will take make the distance file for both the total and nucelar.

I copied these files to /project2/gilad/briana/threeprimeseq/data/ApaQTLs. I will put the QC files here as well.

getDistPeakEnd2QTL.py

#usage getDistPeakEnd2QTL.py "Total" or getDistPeakEnd2QTL.py "Nuclear"


def main(inFile, outFile):
   iFile=open(inFile, "r")
   oFile=open(outFile, "w")  
   oFile.write("PeakID\tPeakEnd\tGene\tGeneStrand\tSNP_chr\tSNP_loc\tEffectSize\tBH\tDistance\n")
   for ln in iFile:
      pid= ln.split()[0]
      peakStrand=pid.split(":")[3].split("_")[1]
      if peakStrand=="+":
          strand = "-"
          end = int(pid.split(":")[1])
      else: 
          strand = "+"
          end = int(pid.split(":")[2])
      gene=pid.split(":")[3].split("_")[0]
      peak=pid.split(":")[3].split("_")[2]
      SNP_Chr=ln.split()[5].split(":")[0]
      SNP_loc=int(ln.split()[5].split(":")[1])
      effectSize=ln.split()[8]
      BH=ln.split()[11]
      Dist= end - SNP_loc
      oFile.write("%s\t%d\t%s\t%s\t%s\t%d\t%s\t%s\t%d\n"%(peak, end, gene, strand, SNP_Chr, SNP_loc, effectSize, BH, Dist))
   oFile.close()  
   
   
if __name__ == "__main__":
    import sys
    fraction = sys.argv[1]
    inFile = "/project2/gilad/briana/threeprimeseq/data/ApaQTLs/%sapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt"%(fraction)
    outFile = "/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Distance2EndPeak.%s.apaQTLs.txt"%(fraction)
    main(inFile, outFile)

Plot for total:

TotDist=read.table("../data/ApaQTLs/Distance2EndPeak.Total.apaQTLs.txt", header=T) %>% mutate(Fraction="Total") %>% select(Fraction, Distance)
NucDist=read.table("../data/ApaQTLs/Distance2EndPeak.Nuclear.apaQTLs.txt", header=T)%>% mutate(Fraction="Nuclear") %>% select(Fraction, Distance)

BothDist=data.frame(rbind(TotDist, NucDist))
ggplot(BothDist, aes(x=Distance, by=Fraction, fill=Fraction))+geom_histogram(bins=70, alpha=.5) + scale_fill_manual(values=c("deepskyblue3","darkviolet")) + labs(title="Distance From apaQTL to End of Peak" )

Expand here to see past versions of unnamed-chunk-5-1.png:
Version Author Date
450b389 Briana Mittleman 2019-02-06

Where are the SNP

I want to take all of the SNP locations see what region of the genome they are in. I can use the annotation in /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed. I can do this with bedtools intersect if I make a bedfile for the QTLs.

Goal file: chr, loc -1, loc, peak:QTLgene, BH, geneStrand

I can get all of this information most easily from the distance file I made.

QTLfile2Bed.py

#usage QTLfile2Bed.py "Total" or QTLfile2Bed.py "Nuclear"


def main(inFile, outFile):
   iFile=open(inFile, "r")
   oFile=open(outFile, "w")  
   for num, ln in enumerate(iFile):
       if num > 0:
           peakID, peakend, gene, strand, chr, loc, effect, bh, dist = ln.split()
           start=int(loc) -1
           end= int(loc) 
           name= peakID + ":" + gene
           oFile.write("%s\t%d\t%d\t%s\t%s\t%s\n"%(chr, start, end, name, bh, strand))
   oFile.close()



if __name__ == "__main__":
    import sys
    fraction = sys.argv[1]
    inFile = "/project2/gilad/briana/threeprimeseq/data/ApaQTLs/Distance2EndPeak.%s.apaQTLs.txt"%(fraction)
    outFile = "/project2/gilad/briana/threeprimeseq/data/ApaQTLs/%s.apaQTLs.bed"%(fraction)
    main(inFile, outFile)

I will need to sort the output

sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.bed > /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort.bed

sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.bed > /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort.bed

Look at which regions these map to.

mapQTLs2GenomeLoc.sh

#!/bin/bash

#SBATCH --job-name=mapQTLs2GenomeLoc
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mapQTLs2GenomeLoc.out
#SBATCH --error=mapQTLs2GenomeLoc.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

#annotation: /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed

#QTL nucelar: /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort.bed

#QTL total: /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort.bed


bedtools map -a /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort.bed -b /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed -c 4 -S -o distinct > /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort_GeneAnno.bed


bedtools map -a /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort.bed -b /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed -c 4 -S -o distinct > /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort_GeneAnno.bed

Most of the QTLs are not in any region.

QTLs by coverage

TotalCounts_AllInd=read.table("../data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.5perc.fc", header=T, stringsAsFactors = F) %>% separate(Geneid, into =c('peak', 'chr', 'start', 'end', 'strand', 'gene'), sep = ":") %>% select(-peak, -chr, -start, -end, -strand, -Chr, -Start, -End, -Strand, -Length, -gene) %>% rowMeans()

TotalCounts_AllInd_genes=read.table("../data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.5perc.fc", header=T, stringsAsFactors = F) %>% separate(Geneid, into =c('peak', 'chr', 'start', 'end', 'strand', 'gene'), sep = ":") %>% select(gene)

 
TotalCounts_AllInd_mean=data.frame(cbind(Gene=TotalCounts_AllInd_genes,Exp=TotalCounts_AllInd)) %>% filter(Exp>0)
#remove 0  



TotalCounts_no0_perc= TotalCounts_AllInd_mean %>% mutate(Percentile = percent_rank(Exp)) 

#Seperate by percentile
TotalCounts_no0_perc10= TotalCounts_no0_perc %>% filter(Percentile<.1) 

TotalCounts_no0_perc20= TotalCounts_no0_perc %>% filter(Percentile<.2& Percentile>.1)

TotalCounts_no0_perc30= TotalCounts_no0_perc %>% filter(Percentile<.3& Percentile>.2)

TotalCounts_no0_perc40= TotalCounts_no0_perc %>% filter(Percentile<.4& Percentile>.3)

TotalCounts_no0_perc50= TotalCounts_no0_perc %>% filter(Percentile<.5& Percentile>.4)
TotalCounts_no0_perc60= TotalCounts_no0_perc %>% filter(Percentile<.6& Percentile>.5)

TotalCounts_no0_perc70= TotalCounts_no0_perc %>% filter(Percentile<.7& Percentile>.6)

TotalCounts_no0_perc80= TotalCounts_no0_perc %>% filter(Percentile <.8 & Percentile>.7)
TotalCounts_no0_perc90= TotalCounts_no0_perc %>% filter(Percentile<.9 & Percentile>.8)

TotalCounts_no0_perc100= TotalCounts_no0_perc %>% filter( Percentile>.9)

Now I need to figure out the number of QTL gene in each percentile.

nucQTLGenes=nucQTLs %>% separate(pid, into=c('chr', 'start', 'end', 'id'), sep=":") %>% separate(id, sep="_", into=c("gene", 'strand', 'peak')) %>% select(gene) %>% unique()

totQTLGenes=totQTLs %>% separate(pid, into=c('chr', 'start', 'end', 'id'), sep=":") %>% separate(id, sep="_", into=c("gene", 'strand', 'peak')) %>% select(gene) %>% unique()

Per percent- use

totGene10= totQTLGenes %>% semi_join(TotalCounts_no0_perc10,by="gene") %>%  nrow()
totGene20= totQTLGenes %>% semi_join(TotalCounts_no0_perc20,by="gene")%>%  nrow()
totGene30= totQTLGenes %>% semi_join(TotalCounts_no0_perc30,by="gene")%>%  nrow()
totGene40= totQTLGenes %>% semi_join(TotalCounts_no0_perc40,by="gene")%>% nrow()
totGene50= totQTLGenes %>% semi_join(TotalCounts_no0_perc50,by="gene")%>% nrow()
totGene60= totQTLGenes %>% semi_join(TotalCounts_no0_perc60,by="gene")%>% nrow()
totGene70= totQTLGenes %>% semi_join(TotalCounts_no0_perc70,by="gene")%>%  nrow()
totGene80= totQTLGenes %>% semi_join(TotalCounts_no0_perc80,by="gene")%>%  nrow()
totGene90= totQTLGenes %>% semi_join(TotalCounts_no0_perc90,by="gene")%>%  nrow()
totGene100= totQTLGenes %>% semi_join(TotalCounts_no0_perc100,by="gene")%>%  nrow()


totGene_allPerc=c(totGene10,totGene20,totGene30,totGene40,totGene50,totGene60,totGene70,totGene80, totGene90,totGene100)

plot this:

plot(totGene_allPerc)

Why are more genes included

Session information

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

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  forcats_0.3.0   stringr_1.3.1   dplyr_0.7.6    
 [5] purrr_0.2.5     readr_1.1.1     tidyr_0.8.1     tibble_1.4.2   
 [9] ggplot2_3.0.0   tidyverse_1.2.1 workflowr_1.1.1

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     labeling_0.3      knitr_1.20       
[25] broom_0.5.0       Rcpp_0.12.19      scales_1.0.0     
[28] backports_1.1.2   jsonlite_1.5      hms_0.4.2        
[31] digest_0.6.17     stringi_1.2.4     grid_3.5.1       
[34] rprojroot_1.3-2   cli_1.0.1         tools_3.5.1      
[37] magrittr_1.5      lazyeval_0.2.1    crayon_1.3.4     
[40] whisker_0.3-2     pkgconfig_2.0.2   xml2_1.2.0       
[43] lubridate_1.7.4   assertthat_0.2.0  rmarkdown_1.10   
[46] httr_1.3.1        rstudioapi_0.8    R6_2.3.0         
[49] nlme_3.1-137      git2r_0.23.0      compiler_3.5.1

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