APAqtls with Leafcutter

Last updated: 2018-08-21

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Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    output/.DS_Store

Untracked files:
    Untracked:  analysis/ncbiRefSeq_sm.sort.mRNA.bed
    Untracked:  analysis/snake.config.notes.Rmd
    Untracked:  data/18486.genecov.txt
    Untracked:  data/APApeaksYL.total.inbrain.bed
    Untracked:  data/Totalpeaks_filtered_clean.bed
    Untracked:  data/YL-SP-18486-T-combined-genecov.txt
    Untracked:  data/YL-SP-18486-T_S9_R1_001-genecov.txt
    Untracked:  data/bedgraph_peaks/
    Untracked:  data/bin200.5.T.nuccov.bed
    Untracked:  data/bin200.Anuccov.bed
    Untracked:  data/bin200.nuccov.bed
    Untracked:  data/clean_peaks/
    Untracked:  data/comb_map_stats.csv
    Untracked:  data/comb_map_stats.xlsx
    Untracked:  data/combined_reads_mapped_three_prime_seq.csv
    Untracked:  data/gencov.test.csv
    Untracked:  data/gencov.test.txt
    Untracked:  data/gencov_zero.test.csv
    Untracked:  data/gencov_zero.test.txt
    Untracked:  data/gene_cov/
    Untracked:  data/joined
    Untracked:  data/leafcutter/
    Untracked:  data/merged_combined_YL-SP-threeprimeseq.bg
    Untracked:  data/nuc6up/
    Untracked:  data/perm_QTL/
    Untracked:  data/reads_mapped_three_prime_seq.csv
    Untracked:  data/smash.cov.results.bed
    Untracked:  data/smash.cov.results.csv
    Untracked:  data/smash.cov.results.txt
    Untracked:  data/smash_testregion/
    Untracked:  data/ssFC200.cov.bed
    Untracked:  data/temp.file1
    Untracked:  data/temp.file2
    Untracked:  data/temp.gencov.test.txt
    Untracked:  data/temp.gencov_zero.test.txt
    Untracked:  output/picard/
    Untracked:  output/plots/
    Untracked:  output/qual.fig2.pdf

Unstaged changes:
    Modified:   analysis/28ind.peak.explore.Rmd
    Modified:   analysis/cleanupdtseq.internalpriming.Rmd
    Modified:   analysis/dif.iso.usage.leafcutter.Rmd
    Modified:   analysis/explore.filters.Rmd
    Modified:   analysis/peak.cov.pipeline.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	73516a6	brimittleman	2018-08-21	chr1 results
html	d682ab6	brimittleman	2018-08-21	Build site.
Rmd	a3c44fb	brimittleman	2018-08-21	add code for permute fastqtl
html	5564e25	brimittleman	2018-08-20	Build site.
Rmd	6b1b51c	brimittleman	2018-08-20	start qtl analsis, add to index

I need to run fastQTL to call the apaQTLs.

Imputed snp: /project2/yangili1/tonyzeng/genotyping/imputation_results/ `

module load samtools
#zip file 
gzip filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt 

module load python
#leafcutter script
python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz 

#source activate three-prime-env
sh filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz_prepare.sh

#run for nuclear as well 
gzip filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt 
#unload anaconda, load python
python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz 
#load anaconda and env. 
sh filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz_prepare.sh

#filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.PCs
#filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.PCs

makeSamplelist.py

#make a sample list  

fout = file("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/SAMPLE.txt",'w')

for ln in open("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/file_id_mapping_nuc.txt", "r"):
    bam, sample = ln.split()
    line=sample[:-2]
    fout.write("NA"+line + "\n")
fout.close()

APAqtl_nominal_nuc.sh

#!/bin/bash


#SBATCH --job-name=APAqtl_nominal_nuc
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_nominal_nuc.out
#SBATCH --error=APAqtl_nominal_nuc.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$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.qqnorm_chr$i.nominal.out --chunk 1 1  --window 5e4 --include-samples /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/SAMPLE.txt
done

Remove the non matching ind. from the sample list.

Remove 18500, 19092 and 19193, 18497

Try it on the total ones:

APAqtl_nominal_tot.sh

#!/bin/bash


#SBATCH --job-name=APAqtl_nominal_tot
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_nominal_tot.out
#SBATCH --error=APAqtl_nominal_tot.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$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.qqnorm_chr$i.nominal.out --chunk 1 1  --window 5e4 --include-samples /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/SAMPLE.txt
done

Filter dose files

I need to remove non snps and snps with <.05 from the dosage file.

I will first copy all of the dosage files to my direcory instead of changing tonys.

cp *dose.vcf.gz /project2/gilad/briana/YRI_geno_hg19/

I want to write a python script that will read in the files and perform the filters.

I wrote a python script that take in the dose file and a name of an out file. I will write a bash script to wrap this on all of the chrs.

#!/bin/bash


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

module load python

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 
python filter_vcf.py chr$i.dose.vcf chr$i.dose.filt.vcf
done

Now I can use these for the fastqtl script instead.

I also updated to only use the first 2 pcs as covariates.

Run permuted version

Permutation pass to calculate correctedp-values for molecular phenotypes.

APAqtl_perm_tot.sh

#!/bin/bash


#SBATCH --job-name=APAqtl_perm_tot
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_perm_tot.out
#SBATCH --error=APAqtl_perm_tot.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$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 5e4 --include-samples /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/SAMPLE.txt
done

APAqtl_perm_nuc.sh

#!/bin/bash


#SBATCH --job-name=APAqtl_nominal_nuc
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_perm_nuc.out
#SBATCH --error=APAqtl_perm_nuc.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$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 5e4 --include-samples /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/SAMPLE.txt
done

The results file has the folowing columns:

ID of the tested molecular phenotype (in this particular case, the gene ID)
Number of variants tested in cis for this phenotype
MLE of the shape1 parameter of the Beta distribution
MLE of the shape2 parameter of the Beta distribution
Dummy [To be described later]
ID of the best variant found for this molecular phenotypes (i.e. with the smallest p-value)
Distance between the molecular phenotype - variant pair
The nominal p-value of association that quantifies how significant from 0, the regression coefficient is
The slope associated with the nominal p-value of association [only in version > v2-184]
A first permutation p-value directly obtained from the permutations with the direct method. This is basically a corrected version of the nominal p-value that accounts for the fact that multiple variants are tested per molecular phenotype.
A second permutation p-value obtained via beta approximation. We advice to use this one in any downstream analysis.

I can check the experiments as recomended by the FastQTL site.

d = read.table("permutations.all.chunks.txt.gz", hea=F, stringsAsFactors=F)
colnames(d) = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "ppval", "bpval")
plot(d$ppval, d$bpval, xlab="Direct method", ylab="Beta approximation", main="Check plot")
abline(0, 1, col="red")

I will try this first on the resutls from chr1.

nuc.chr1= read.table("../data/perm_QTL/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt.gz.qqnorm_chr1.perm.out",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))


plot(nuc.chr1$ppval, nuc.chr1$bpval, xlab="Direct method", ylab="Beta approximation", main="Nuclear Check plot")
abline(0, 1, col="red")

tot.chr1=read.table("../data/perm_QTL/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt.gz.qqnorm_chr1.perm.out", head=F, stringsAsFactors = F, col.names= c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))


plot(tot.chr1$ppval, tot.chr1$bpval, xlab="Direct method", ylab="Beta approximation", main="Total Check plot")
abline(0, 1, col="red")

Correct for multiple testing:

Bonferonni

nuc.chr1$bonferroni = p.adjust(nuc.chr1$bpval, method="bonferroni")

plot(-log10(nuc.chr1$bonferroni), main="Nuclear chr1 bonferroni corrected pval")

tot.chr1$bonferroni = p.adjust(tot.chr1$bpval, method="bonferroni")

plot(-log10(tot.chr1$bonferroni),  main="Total chr1 bonferroni corrected pval")

< .05 is 1.3 on this plot.

nuc.chr1$bh=p.adjust(nuc.chr1$bpval, method="fdr")

plot(-log10(nuc.chr1$bh), main="Nuclear chr1 BH corrected pval")

tot.chr1$bh=p.adjust(tot.chr1$bpval, method="fdr")
plot(-log10(tot.chr1$bh), main="Total chr1 BH corrected pval")

10% FDR is 1 on this plot.

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     

loaded via a namespace (and not attached):
 [1] workflowr_1.1.1   Rcpp_0.12.18      digest_0.6.15    
 [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_2.6.0     rmarkdown_1.10    tools_3.5.1      
[16] stringr_1.3.1     yaml_2.1.19       compiler_3.5.1   
[19] htmltools_0.3.6   knitr_1.20

This reproducible R Markdown analysis was created with workflowr 1.1.1

APAqtls with Leafcutter

Briana Mittleman

8/15/2018

Filter dose files

Run permuted version

Session information