QTLs by number of Included PCs

Last updated: 2018-12-10

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File	Version	Author	Date	Message
Rmd	28e2214	Briana Mittleman	2018-12-10	start qtls by included PC analysis

In this analysis I will rerun the FastQTL APAqtl calling including different PCs. In the original analysis I am including 2 PCs as covariates. First I will run without any PCs, then with 1, then with just the second 1.

PCs are in /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/

APAqtl_nominal_transcript_noPCs.sh

#!/bin/bash


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

for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans_noPC/NOPC_filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.nominal.out --chunk 1 1  --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done


for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz  --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans_noPC/NOPC_filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.nominal.out --chunk 1 1  --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done

permuted

APAqtl_permuted_transcript_noPCs.sh

#!/bin/bash


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

for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --permute 1000  --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz  --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans_noPC/NO_PCfiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done


for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --permute 1000  --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans_noPC/NOPC_filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done

I can take the results and run it through the R script that give the BH corrected PValues. I will modify this to just output the significant ones at fdr 10%.

nQTLfromDifPC.R

library(dplyr)
library(tidyr)
library(ggplot2)
library(readr)
library(optparse)

#script takes the permuted resutls for total and nuclear then a PC info identifier. It iwll give the significant qtls for that condition  


option_list = list(
    make_option(c("-N", "--NucRes"), action="store", default=NA, type='character', help="nuclearRes"),
    make_option(c("-T", "--TotRes"), action="store", default=NA, type='character', help="totalRes"),
    make_option(c("-P", "--PC"), action="store", default=NA, type='character', help="PCs used in analysis")
    
)

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

##total results
tot.perm= read.table(opt$TotRes,head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))

#BH correction
tot.perm$bh=p.adjust(tot.perm$bpval, method="fdr")


tot.perm=tot.perm %>% filter(-log10(bh) > 1)
#write df with BH  

write.table(tot.perm, file =paste("/project2/gilad/briana/threeprimeseq/data/diffPCAnalysis/", opt$PC, "PCs.totQTL", sep=""), col.names = T, row.names = F, quote = F)

##nuclear results  


nuc.perm= read.table(opt$NucRes,head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.perm$bh=p.adjust(nuc.perm$bpval, method="fdr")

nuc.perm=nuc.perm %>% filter(-log10(bh) > 1)

# write df with BH
write.table(nuc.perm, file =paste("/project2/gilad/briana/threeprimeseq/data/diffPCAnalysis/", opt$PC, "PCs.NucQTL", sep=""), col.names = T, row.names = F, quote = F)

Script to run this:
nQTLfromDifPC.0PCs.sh

#!/bin/bash


#SBATCH --job-name=nQTLfromDifPC.0PCs
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=nQTLfromDifPC.0PCs.out
#SBATCH --error=nQTLfromDifPC.0PCs.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


Rscript nQTLfromDifPC.R -N NUCRES -T TOTRES -P "0"

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     

loaded via a namespace (and not attached):
 [1] workflowr_1.1.1   Rcpp_0.12.19      digest_0.6.17    
 [4] rprojroot_1.3-2   R.methodsS3_1.7.1 backports_1.1.2  
 [7] git2r_0.23.0      magrittr_1.5      evaluate_0.11    
[10] stringi_1.2.4     whisker_0.3-2     R.oo_1.22.0      
[13] R.utils_2.7.0     rmarkdown_1.10    tools_3.5.1      
[16] stringr_1.3.1     yaml_2.2.0        compiler_3.5.1   
[19] htmltools_0.3.6   knitr_1.20

This reproducible R Markdown analysis was created with workflowr 1.1.1

QTLs by number of Included PCs

Briana Mittleman

12/10/2018

Session information