apaQTLs

Last updated: 2019-09-04

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Knit directory: apaQTL/analysis/

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Unstaged changes:
    Modified:   analysis/NuclearSpecAPAqtl.Rmd
    Modified:   analysis/PrematureTermQTL.Rmd
    Modified:   analysis/Readdistagainstfeatures.Rmd
    Modified:   analysis/overlapapaqtlsandeqtls.Rmd
    Modified:   analysis/rerunQTL_changePC.Rmd
    Modified:   analysis/version15bpfilter.Rmd
    Modified:   code/BothFracDTPlotGeneRegions.sh
    Modified:   code/Snakefile
    Modified:   code/SnakefilefiltPAS
    Modified:   code/apaQTLCorrectPvalMakeQQ.R
    Modified:   code/apaQTL_Nominal.sh
    Modified:   code/apaQTL_permuted.sh
    Modified:   code/apaQTLsnake.err
    Modified:   code/bam2bw.sh
    Modified:   code/bed2saf.py
    Modified:   code/cluster.json
    Modified:   code/clusterfiltPAS.json
    Modified:   code/config.yaml
    Modified:   code/environment.yaml
    Modified:   code/makePheno.py
    Modified:   code/mergeAllBam.sh
    Modified:   code/mergeByFracBam.sh
    Modified:   code/mergePeaks.sh
    Modified:   code/peakFC.sh
    Modified:   code/snakemake.batch
    Modified:   code/snakemakePAS.batch
    Modified:   code/snakemakefiltPAS.batch
    Modified:   code/submit-snakemake.sh
    Modified:   code/submit-snakemakePAS.sh
    Modified:   code/submit-snakemakefiltPAS.sh
    Deleted:    code/test.txt
    Modified:   data/MetaDataSequencing.txt
    Modified:   docs/figure/signalsiteanalysis.Rmd/figure1bMain-1.pdf
    Deleted:    reads_graphs.Rmd

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
Rmd	41d1961	brimittleman	2019-09-04	wflow_publish(c(“analysis/signalsiteanalysis.Rmd”, “analysis/corrbetweenind.Rmd”,
html	c17ed4a	brimittleman	2019-06-13	Build site.
Rmd	b7c8ed1	brimittleman	2019-06-13	fix big bugg
html	71146f1	brimittleman	2019-04-29	Build site.
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
html	ed97e35	brimittleman	2019-04-21	Build site.
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 ──

✔ ggplot2 3.1.1       ✔ purrr   0.3.2  
✔ tibble  2.1.1       ✔ dplyr   0.8.0.1
✔ tidyr   0.8.3       ✔ stringr 1.3.1  
✔ readr   1.3.1       ✔ forcats 0.3.0

── Conflicts ───────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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library(reshape2)


Attaching package: 'reshape2'

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library(workflowr)

This is workflowr version 1.4.0
Run ?workflowr for help getting started

library(cowplot)


Attaching package: 'cowplot'

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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
#python2 

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.

#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:

#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

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

Rscript 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] 377

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   1.216   1.000   3.000

hist(TotQTLs_gene$nQTL)

Version	Author	Date
c17ed4a	brimittleman	2019-06-13
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] 564