Last updated: 2019-04-29

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Rmd 3c5e041 brimittleman 2019-04-29 add write out for qtls
html 9490b23 brimittleman 2019-04-29 Build site.
Rmd b18b96c brimittleman 2019-04-29 fix distance
html 2d33728 brimittleman 2019-04-28 Build site.
Rmd 7416404 brimittleman 2019-04-28 add res
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Rmd be90ded brimittleman 2019-04-21 fix to 5perc phenp
html 28bd046 brimittleman 2019-04-18 Build site.
Rmd 017f5c0 brimittleman 2019-04-18 add map apa qtl pipeline 
library(tidyverse)
── Attaching packages ─────────────────────────────────────────────── tidyverse 1.2.1 ──
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library(reshape2)

Attaching package: 'reshape2'
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library(workflowr)
This is workflowr version 1.3.0
Run ?workflowr for help getting started
library(cowplot)

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    ggsave

In this analysis I will call apaQTls in both fractions. I will start with the phenotype files and normalized the counts using the leafcutter package in order to run the fastq QTL mapper.

Prepare phenotypes for QTL- phenotype dir

It is best to run this analysis in the data/phenotype_5perc directory. I have copied the leafcutter prepare_phenotype_table.py to the code directroy to use here.

#!/bin/bash
module load python

gzip APApeak_Phenotype_GeneLocAnno.Total.5perc.fc
gzip APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc

python ../../code/prepare_phenotype_table.py APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz
python ../../code/prepare_phenotype_table.py APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz

This will output bash scripts to run.

module load Anaconda3
source activate three-prime-env

sh APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz_prepare.sh
sh APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz_prepare.sh

Subset the PCs to use the first 2 in the qtl calling:

module load Anaconda3
source activate three-prime-env

head -n 3 APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.PCs > APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.2PCs
head -n 3 APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.PCs > APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.2PCs

Call QTLs- code dir

Next I will need to make a sample list. From the code directory:

python makeSampleList.py

remove 19092 and 19193

Prepare directroy

mkdir ../data/apaQTLNominal
mkdir ../data/apaQTLPermuted

Run the code to call QTLs within 1mb of each PAS peak. I run both a nominal pass and a permuted pas. The permulted pas chosses the best snp for each peak gene pair.

sbatch apaQTL_Nominal.sh
sbatch apaQTL_permuted.sh

Concatinate all of the results in the permuted set. I do this so I can account for multiple testing with the benjamini hochberg test.

Concatinate results in permuted directory:

cat APApeak_Phenotype_GeneLocAnno.Total.5perc.fc.gz.qqnorm_chr* > APApeak_Phenotype_GeneLocAnno.Total_permRes.txt

cat APApeak_Phenotype_GeneLocAnno.Nuclear.5perc.fc.gz.qqnorm_chr* > APApeak_Phenotype_GeneLocAnno.Nuclear_permRes.txt

Run correction script

Rscripts apaQTLCorrectPvalMakeQQ.R

Evaluation results

totRes=read.table("../data/apaQTLPermuted/APApeak_Phenotype_GeneLocAnno.Total_permResBH.txt", stringsAsFactors = F, header = T) %>% separate(pid, into=c("Chr", "Start", "End", "PeakID"), sep=":") %>% separate(PeakID, into=c("Gene", "Loc", "Strand","Peak"), sep="_")

Total Apa QTLs

TotQTLs= totRes %>% filter(-log10(bh)>=1)
nrow(TotQTLs)
[1] 502

apaQTL genes:

TotQTLs_gene=TotQTLs %>% group_by(Gene)  %>% summarise(nQTL=n())

summary(TotQTLs_gene$nQTL)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   1.000   1.000   1.307   2.000   6.000 
hist(TotQTLs_gene$nQTL)

Version Author Date
9490b23 brimittleman 2019-04-29
2d33728 brimittleman 2019-04-28

Location distribution for peaks:

TotQTLs_loc= TotQTLs %>% group_by(Loc) %>% summarise(nLoc=n()) %>% mutate(PropLoc=nLoc/nrow(TotQTLs))


totQTLloc=ggplot(TotQTLs_loc, aes(x=Loc, y=PropLoc, fill=Loc)) + geom_bar(stat = "Identity") + labs(x="Location of Significant Peak", y="Proportion of QTLs", title="Total QTL peak distribution")+ theme(axis.text.x = element_text(angle = 90, hjust = 1))
nucRes=read.table("../data/apaQTLPermuted/APApeak_Phenotype_GeneLocAnno.Nuclear_permResBH.txt", stringsAsFactors = F, header = T) %>% separate(pid, into=c("Chr", "Start", "End", "PeakID"), sep=":") %>% separate(PeakID, into=c("Gene", "Loc", "Strand","Peak"), sep="_")

Nuclear Apa QTLs

NucQTLs= nucRes %>% filter(-log10(bh)>=1)
nrow(NucQTLs)
[1] 1070

apaQTL genes:

NucQTLs_gene= NucQTLs %>% group_by(Gene)  %>% summarise(nQTL=n())

summary(NucQTLs_gene$nQTL)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  1.000   1.000   1.000   1.328   2.000   7.000 
hist(NucQTLs_gene$nQTL)

Version Author Date
9490b23 brimittleman 2019-04-29
2d33728 brimittleman 2019-04-28

Location distribution for peaks:

NucQTLs_loc= NucQTLs %>% group_by(Loc) %>% summarise(nLoc=n()) %>% mutate(PropLoc=nLoc/nrow(NucQTLs))


nucQTLloc=ggplot(NucQTLs_loc, aes(x=Loc, y=PropLoc, fill=Loc)) + geom_bar(stat = "Identity") + labs(x="Location of Significant Peak", y="Proportion of QTLs", title="Nuclear QTL peak distribution")+theme(axis.text.x = element_text(angle = 90, hjust = 1))
plot_grid(totQTLloc, nucQTLloc)

Version Author Date
9490b23 brimittleman 2019-04-29
2d33728 brimittleman 2019-04-28

Distance to PAS

The distance to PAS is the location of the snp to the end of the peak for the

Strand in this file is the peak strand (opposite of gene). This means for + strand I want the start of the Peak and for the - strand i will use the end of the peak. ###Total

TotQTLs_dist=TotQTLs %>% separate(sid, into=c("SnpCHR", "SNPpos"), sep=":") %>% mutate(Dist2PAS=ifelse(Strand=="+", as.integer(SNPpos)-as.integer(Start), as.integer(SNPpos)-as.integer(End)))


summary(abs(TotQTLs_dist$Dist2PAS))
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      0    1598    6160    8832   15461   25071

Plot:

totqtldist=ggplot(TotQTLs_dist, aes(x=Dist2PAS)) + geom_histogram(bins=100, fill="darkviolet") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(x="Distance from QTL snp to PAS",y="Number of Total apaQTLs", title="Total apaQTL \n are close to the PAS they regulate")
ggplot(TotQTLs_dist, aes(x=Dist2PAS)) + geom_histogram(bins=100) + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + facet_grid(~Loc)

Version Author Date
9490b23 brimittleman 2019-04-29

Nuclear

NucQTLs_dist=NucQTLs %>% separate(sid, into=c("SnpCHR", "SNPpos"), sep=":") %>% mutate(Dist2PAS=ifelse(Strand=="+", as.integer(SNPpos)-as.integer(Start), as.integer(SNPpos)-as.integer(End)))


summary(abs(NucQTLs_dist$Dist2PAS))
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
      0    1930    7726    9216   15463   25071

Plot:

nucqtldist=ggplot(NucQTLs_dist, aes(x=Dist2PAS)) + geom_histogram(bins=100,fill="deepskyblue3") + theme(axis.text.x = element_text(angle = 90, hjust = 1)) + labs(x="Distance from QTL snp to PAS",y="Number of Nuclear apaQTLs", title="Nuclear apaQTL \n are close to the PAS they regulate") 
plot_grid(totqtldist,nucqtldist)

Write out QTLs:

write.table(TotQTLs, file="../data/apaQTLs/Total_apaQTLs_5fdr.txt", col.names = T, row.names = F, quote=F)

write.table(NucQTLs, file="../data/apaQTLs/Nuclear_apaQTLs_5fdr.txt", col.names = T, row.names = F, quote=F)

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] cowplot_0.9.4   workflowr_1.3.0 reshape2_1.4.3  forcats_0.3.0  
 [5] stringr_1.3.1   dplyr_0.8.0.1   purrr_0.3.2     readr_1.3.1    
 [9] tidyr_0.8.3     tibble_2.1.1    ggplot2_3.1.0   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 pillar_1.3.1     compiler_3.5.1  
 [5] git2r_0.23.0     plyr_1.8.4       tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.12    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.10  yaml_2.2.0       haven_1.1.2     
[21] withr_2.1.2      xml2_1.2.0       httr_1.3.1       knitr_1.20      
[25] hms_0.4.2        generics_0.0.2   fs_1.2.6         rprojroot_1.3-2 
[29] grid_3.5.1       tidyselect_0.2.5 glue_1.3.0       R6_2.3.0        
[33] readxl_1.1.0     rmarkdown_1.10   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_0.5.1     
[49] crayon_1.3.4