Last updated: 2019-02-15

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    Modified:   analysis/28ind.peak.explore.Rmd
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    Modified:   analysis/pipeline_55Ind.Rmd
    Modified:   analysis/swarmPlots_QTLs.Rmd
    Modified:   analysis/test.max2.Rmd
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    Modified:   analysis/understandPeaks.Rmd
    Modified:   code/Snakefile

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
html ea3cfeb Briana Mittleman 2018-09-26 Build site.
Rmd 1c62f0b Briana Mittleman 2018-09-26 add distribution of distance
html cd3bdf8 Briana Mittleman 2018-09-26 Build site.
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
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── 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 1.2.0
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")

Version Author Date
cd3bdf8 Briana Mittleman 2018-09-26 
plot(-log10(tot_permBH$bh), main="Total BH corrected pval")
abline(h=1,col="Red")

Version Author Date
cd3bdf8 Briana Mittleman 2018-09-26

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")

Version Author Date
cd3bdf8 Briana Mittleman 2018-09-26 
plot(-log10(nuc_permBH$bh), main="Nuclear BH corrected pval")
abline(h=1,col="Red")

Version Author Date
cd3bdf8 Briana Mittleman 2018-09-26

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")

Version Author Date
cd3bdf8 Briana Mittleman 2018-09-26

Explore QTLs

Look at distribution of SNP to peak in each fraction:

ggplot(nuc_qtl_10, aes(x=log10(abs(dist) + 1)) )+ geom_histogram(binwidth=.15, alpha=.5 ) + geom_histogram(data=tot_qtl_10, aes(x=log10(abs(dist) + 1)),fill="Red", alpha=.5,binwidth=.15)  +  annotate("text", x=1, y=950, col="Red", label="Total") + annotate("text", x=1, y=900, col="Black", label="Nuclear") + geom_rect(linetype=1, xmin=.5, xmax=1.5, ymin=850, ymax=1000, color="Black", alpha=0)

Version Author Date
ea3cfeb Briana Mittleman 2018-09-26 
ggplot(nuc_qtl_10, aes(x=log10(abs(dist) + 1)) )+ geom_density( alpha=.25 ,fill="Black") + geom_density(data=tot_qtl_10, aes(x=log10(abs(dist) + 1)),fill="Red", alpha=.25)  + annotate("text", x=1, y=.77, col="Red", label="Total") + annotate("text", x=1, y=.72, col="Black", label="Nuclear") + geom_rect(linetype=1, xmin=.5, xmax=1.5, ymin=.69, ymax=.8, color="Black", alpha=0)

Version Author Date
ea3cfeb Briana Mittleman 2018-09-26 


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  workflowr_1.2.0 reshape2_1.4.3  forcats_0.3.0  
 [5] stringr_1.4.0   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] 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