Last updated: 2019-02-15
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Knit directory: threeprimeseq/analysis/
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Modified: code/Snakefile
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File | Version | Author | Date | Message |
---|---|---|---|---|
html | b08b424 | Briana Mittleman | 2019-02-14 | Build site. |
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 1.2.0
Run ?workflowr for help getting started
library(tidyverse)
── Attaching packages ───────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
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✔ tibble 1.4.2 ✔ dplyr 0.7.6
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✔ readr 1.1.1 ✔ forcats 0.3.0
Warning: package 'stringr' was built under R version 3.5.2
── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
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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)
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" )
Version | Author | Date |
---|---|---|
450b389 | Briana Mittleman | 2019-02-06 |
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.
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)
Version | Author | Date |
---|---|---|
b08b424 | Briana Mittleman | 2019-02-14 |
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_1.4.0 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_1.2.0
loaded via a namespace (and not attached):
[1] Rcpp_0.12.19 cellranger_1.1.0 plyr_1.8.4 compiler_3.5.1
[5] pillar_1.3.0 git2r_0.24.0 bindr_0.1.1 tools_3.5.1
[9] digest_0.6.17 lubridate_1.7.4 jsonlite_1.6 evaluate_0.13
[13] nlme_3.1-137 gtable_0.2.0 lattice_0.20-35 pkgconfig_2.0.2
[17] rlang_0.2.2 cli_1.0.1 rstudioapi_0.9.0 yaml_2.2.0
[21] haven_1.1.2 withr_2.1.2 xml2_1.2.0 httr_1.3.1
[25] knitr_1.20 hms_0.4.2 fs_1.2.6 rprojroot_1.3-2
[29] grid_3.5.1 tidyselect_0.2.4 glue_1.3.0 R6_2.3.0
[33] readxl_1.1.0 rmarkdown_1.11 modelr_0.1.2 magrittr_1.5
[37] whisker_0.3-2 backports_1.1.2 scales_1.0.0 htmltools_0.3.6
[41] rvest_0.3.2 assertthat_0.2.0 colorspace_1.3-2 labeling_0.3
[45] stringi_1.2.4 lazyeval_0.2.1 munsell_0.5.0 broom_0.5.0
[49] crayon_1.3.4