ApaQTLs with all individuals

Last updated: 2018-09-26

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    Untracked:  analysis/callMolQTLS.Rmd
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Unstaged changes:
    Modified:   analysis/28ind.peak.explore.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/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	529ace6	Briana Mittleman	2018-09-26	add QTL res
html	b1bcf99	Briana Mittleman	2018-09-25	Build site.
Rmd	f4e1942	Briana Mittleman	2018-09-25	initiate all ind QTL analysis

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

I am using the code from peakOverlap_oppstrand.Rmd analysis to call QTLs on the full set of individuals. (still missing 4 due to genotype issues- Remove 18500, 19092 and 19193, 18497 - at 35).

Scripts:
* APAqtl_nominal_oppstrand.sh

APAqtl_perm_Opp.sh

cat /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total* > /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_permRes.txt

cat /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear* > /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_permRes.txt

Write a script to ad the BH correction of the permuted QTL pvalues. I will write the plots to

APAqtlpermCorrectQQplot.R

library(dplyr)


##total results
tot.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_permRes.txt",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")

#plot qqplot
pdf("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_total_APAperm.pdf") 
qqplot_total= qqplot(-log10(runif(nrow(tot.perm))), -log10(tot.perm$bpval),ylab="-log10 Total permuted pvalue", xlab="Uniform expectation", main="Total permuted pvalues for all snps")
abline(0,1)
dev.off()

#write df with BH  

write.table(tot.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_permResBH.txt", col.names = T, row.names = F, quote = F)

##nuclear results  


nuc.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_permRes.txt",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")


#plot qqplot
pdf("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_nuclear_APAperm.pdf") 
qqplot(-log10(runif(nrow(nuc.perm))), -log10(nuc.perm$bpval),ylab="-log10 Nuclear permuted pvalue", xlab="Uniform expectation", main="Nuclear permuted pvalues for all snps")
abline(0,1)
dev.off()

# write df with BH
write.table(nuc.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_Opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_permResBH.txt", col.names = T, row.names = F, quote = F)

Write a script to run this:

run_APAqtlpermCorrectQQplot.sh

#!/bin/bash


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

module load Anaconda3
source activate three-prime-env


Rscript APAqtlpermCorrectQQplot.R

Total results

tot_permBH=read.table("../data/perm_QTL_opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_permResBH.txt", header=T, stringsAsFactors = F)

Check to quality of the tests:

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

plot(-log10(tot_permBH$bh), main="Total BH corrected pval")
abline(h=1,col="Red")

I am going to look how many variants pass the 10% FDR.

tot_qtl_10= tot_permBH %>% filter(-log10(bh) > 1) %>% nrow()
tot_qtl_10

[1] 1468

This is not accounting for the same peak in multiple genes. I want to look at the number of unique snps that are significant.

tot_qtl_10uniq= tot_permBH %>% filter(-log10(bh) > 1)  %>% summarise(n_distinct(sid)) 
tot_qtl_10uniq

  n_distinct(sid)
1             568

Nuclear results

nuc_permBH=read.table("../data/perm_QTL_opp/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_permResBH.txt", header=T, stringsAsFactors = F)

Check to quality of the tests:

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

plot(-log10(nuc_permBH$bh), main="Nuclear BH corrected pval")
abline(h=1,col="Red")

I am going to look how many variants pass the 10% FDR.

nuc_qtl_10= nuc_permBH %>% filter(-log10(bh) > 1) %>% nrow()
nuc_qtl_10

[1] 7025

This is not accounting for the same peak in multiple genes. I want to look at the number of unique snps that are significant.

nuc_qtl_10uniq= nuc_permBH %>% filter(-log10(bh) > 1)  %>% summarise(n_distinct(sid)) 
nuc_qtl_10uniq

  n_distinct(sid)
1            2736

Compare number of sig QTLs by FDR cuttoff

nQTL_tot=c()
FDR=seq(.05, .5, .01)
for (i in FDR){
  x=tot_permBH %>% filter(bh < i ) %>% nrow()
  nQTL_tot=c(nQTL_tot, x)
}

FDR=seq(.05, .5, .01)
nQTL_nuc=c()
for (i in FDR){
  x=nuc_permBH %>% filter(bh < i ) %>% nrow()
  nQTL_nuc=c(nQTL_nuc, x)
}

nQTL=as.data.frame(cbind(FDR, Total=nQTL_tot, Nuclear=nQTL_nuc))
nQTL_long=melt(nQTL, id.vars = "FDR")

ggplot(nQTL_long, aes(x=FDR, y=value, by=variable, col=variable)) + geom_line(size=1.5) + labs(y="Number of Significant QTLs", title="APAqtls detected by FDR cuttoff", color="Fraction")

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     labeling_0.3      knitr_1.20       
[25] broom_0.5.0       Rcpp_0.12.18      scales_1.0.0     
[28] backports_1.1.2   jsonlite_1.5      hms_0.4.2        
[31] digest_0.6.16     stringi_1.2.4     grid_3.5.1       
[34] rprojroot_1.3-2   cli_1.0.0         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_0.7    R6_2.2.2         
[49] nlme_3.1-137      git2r_0.23.0      compiler_3.5.1

This reproducible R Markdown analysis was created with workflowr 1.1.1

ApaQTLs with all individuals

Briana Mittleman

9/25/2018

Total results

Nuclear results

Compare number of sig QTLs by FDR cuttoff

Session information